Table of Contents

Papers

2014

  1. Arima C, Kajino T, Tamada Y, Imoto S, Shimada Y, Nakatochi M, Suzuki M, Isomura H, Yatabe Y, Yamaguchi T, Yanagisawa K, Miyano S, Takahashi T. Lung adenocarcinoma subtypes definable by lung development-related miRNA expression profiles in association with clinicopathologic features. Carcinogenesis. 35(10): 2224-2231, 2014.
  2. Barclay SS, Tamura T, Ito H, Fujita K, Tagawa K, Shimamura T, Katsuta A, Shiwaku H, Sone M, Imoto S, Miyano S, Okazawa H. Systems biology analysis of Drosophila in vivo screen data elucidates core networks for DNA damage repair in SCA1. Hum Mol Genet. 23(5):1345-1364, 2014.
  3. Becker H, Yoshida K, Blagitko-Dorfs N, Claus R, Pantic M, Abdelkarim M, Niemöller C, Greil C, Hackanson B, Shiraishi Y, Chiba K, Tanaka H, Miyano S, Döhner K, Schnittger S, Henneke P, Niemeyer CM, Flotho C, Pfeifer D, Ogawa S, Lübbert M. Tracing the development of acute myeloid leukemia in CBL syndrome. Blood. 123(12):1883-1886, 2014.
  4. Chiba K, Shiraishi Y, Nagata Y, Yoshida K, Imoto S, Ogawa S, Miyano S. Genomon ITDetector: A tool for somatic internal tandem duplication detection from cancer genome sequencing data. Bioinformatics. In press.
  5. Damm F, Mylonas E, Cosson A, Yoshida K, Della Valle V, Mouly E, Diop M, Scourzic L, Shiraishi Y, Chiba K, Tanaka H, Miyano S, Kikushige Y, Davi F, Lambert J, Gautheret D, Merle-Béral H, Sutton L, Dessen P, Solary E, Akashi K, Vainchenker W, Mercher T, Droin N, Ogawa S, Nguyen-Khac F, Bernard OA. Acquired initiating mutations in early hematopoietic cells of CLL patients. Cancer Discov. 4(9):1088-1101, 2014.
  6. ElGokhy SM, Shibuya T, Shoukry A. Improving miRNA classification using an exhaustive set of features. Advances in Intelligent Systems and Computing. 294:31-39, 2014.
  7. Fang H, Yamaguchi R, Liu X, DaigoY, Yew PY, Tanikawa C, Matsuda K, Imoto S, Miyano S, Nakamura Y. Quantitative T cell repertoire analysis by deep cDNA sequencing of T cell receptor α and β chains using next-generation sequencing (NGS). OncoImmunology. In press.
  8. Fujita A, Miyano S. A tutorial to identify nonlinear associations in gene expression time series data. Methods Mol Biol. 1164:87-95, 2014.
  9. Haferlach T, Nagata Y, Grossmann V, Okuno Y, Bacher U, Nagae G, Schnittger S, Sanada M, Kon A, Alpermann T, Yoshida K, Roller A, Nadarajah N, Shiraishi Y, Shiozawa Y, Chiba K, Tanaka H, Koeffler HP, Klein HU, Dugas M, Aburatani H, Kohlmann A, Miyano S, Haferlach C, Kern W, Ogawa S. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia. 28(2):241-247, 2014.
  10. Hasegawa S, Imai K, Yoshida K, Okuno Y, Muramatsu H, Shiraishi Y, Chiba K, Tanaka H, Miyano S, Kojima S, Ogawa S, Morio T, Mizutani S, Takagi M. Whole-exome sequence analysis of ataxia telangiectasia-like phenotype. J Neurol Sci. 340(1-2):86-90, 2014.
  11. Hasegawa T, Mori T, Yamaguchi R, Imoto S, Miyano S, Akutsu T. An efficient data assimilation schema for restoration and extension of gene regulatory networks using time-course observation data. J Comput Biol. 21(11):785-798, 2014.
  12. Hasegawa T, Nagasaki M, Yamaguchi R, Imoto S, Miyano S. An efficient method of exploring simulation models by assimilating literature and biological observational data. Biosystems. 121:54-66, 2014.
  13. Hasegawa T, Yamaguchi R, Nagasaki M, Miyano S, Imoto S. Inference of gene regulatory networks incorporating multi-source biological knowledge via a state space model with L1 regularization. PLoS One. 9(8): e105942, 2014.
  14. Hoshino A, Nomura K, Hamashima T, Isobe T, Seki M, Hiwatari M, Yoshida K, Shiraishi Y, Chiba K, Tanaka H, Miyano S, Ogawa S, Takita J, Kanegane H. Aggressive transformation of anaplastic large cell lymphoma with increased number of ALK-translocated chromosomes. Int J Hematol. In press.
  15. Hosono N, Makishima H, Jerez A, Yoshida K, Przychodzen B, McMahon S, Shiraishi Y, Chiba K, Tanaka H, Miyano S, Sanada M, Gómez-Seguí I, Verma AK, McDevitt MA, Sekeres MA, Ogawa S, Maciejewski JP. Recurrent genetic defects on chromosome 7q in myeloid neoplasms. Leukemia. 28(6):1348-1351, 2014.
  16. Ito H, Shiwaku H, Yoshida C, Homma H, Luo H, Chen X, Fujita K, Musante L, Fischer U, Frints SGM, Romano C, Ikeuchi Y, Shimamura T, Imoto S, Miyano S, Muramatsu S, Kawauchi T, Hoshino M, Sudol M, Arumughan A, Wanker EE, Rich T, Schwartz C, Matsuzaki F, Bonni A, Kalscheuer VM, Okazawa H. In utero gene therapy rescues microcephaly caused by Pqbp1-hypofunction in neural stem progenitor cells. Mol Psychiatry. In press.
  17. Katayama K, Yamaguchi R, Imoto S, Watanabe K, Miyano S. Analysis of questionnaire for traditional medicine and development of decision support system. Evid Based Complement Alternat Med. 2014:974139, 2014.
  18. Kawashima-Goto S, Imamura T, Seki M, Kato M, Yoshida K, Sugimoto A, Kaneda D, Fujiki A, Miyachi M, Nakatani T, Osone S, Ishida H, Taki T, Takita J, Shiraishi Y, Chiba K, Tanaka H, Miyano S, Ogawa S, Hosoi H. Identification of a homozygous JAK3 V674A mutation caused by acquired uniparental disomy in a relapsed early T-cell precursor ALL patient. Int J Hematol. In press.
  19. Kihara R, Nagata Y, Kiyoi H, Kato T, Yamamoto E, Suzuki K, Chen F, Asou N, Ohtake S, Miyawaki S, Miyazaki Y, Sakura T, Ozawa Y, Usui N, Kanamori H, Kiguchi T, Imai K, Uike N, Kimura F, Kitamura K, Nakaseko C, Onizuka M, Takeshita A, Ishida F, Suzushima H, Kato Y, Miwa H, Shiraishi Y, Chiba K, Tanaka H, Miyano S, Ogawa S, Naoe T. Comprehensive analysis of genetic alterations and their prognostic impacts in adult acute myeloid leukemia patients. Leukemia. 28(8):1586-1595, 2014.
  20. Kurtovic-Kozaric A, Przychodzen B, Singh J, Konarska MM, Clemente MJ, Otrock ZK, Nakashima M, Hsi ED, Yoshida K, Shiraishi Y, Chiba K, Tanaka H, Miyano S, Ogawa S, Boultwood J, Makishima H, Maciejewski JP, Padgett RA. PRPF8 defects cause missplicing in myeloid malignancies. Leukemia. In press.
  21. Li C, Nagasaki M, Ikeda E, Sekiya Y, Miyano S. CSML2SBML: a novel tool for converting quantitative biological pathway models from CSML into SBML. Biosystems. 121:22-28, 2014.
  22. Lin TL, Nagata Y, Kao HW, Sanada M, Okuno Y, Huang CF, Liang DC, Kuo MC, Lai CL, Lee EH, Shih YS, Tanaka H, Shiraishi Y, Chiba K, Lin TH, Wu JH, Miyano S, Ogawa S, Shih LY.　Clonal leukemic evolution in myelodysplastic syndromes with TET2 and IDH1/2 mutations.　Haematologica. 99(1):28-36, 2014.
  23. Matsunawa M, Yamamoto R, Sanada M, Sato-Otsubo A, Shiozawa Y, Yoshida K, Otsu M, Shiraishi Y, Miyano S, Isono K, Koseki H, Nakauchi H, Ogawa S. Haploinsufficiency of Sf3b1 leads to compromised stem cell function but not to myelodysplasia. Leukemia. 28(9):1844-1850, 2014.
  24. Nishiura, H., Ejima, K., Mizumoto, K., Nakaoka, S., Inaba, H., Imoto, S., Yamaguchi, R., Saito, M.M. Cost-effective length and timing of school closure during an influenza pandemic depend on the severity. Theor Biol Med Model. 11:5, 2014.
  25. Ohtsuki T, Nariai N, Kojima K, Mimori T, Sato Y, Kawai Y, Yamaguchi-Kabata Y, Shibuya T, Nagasaki M. SVEM: a structural variant estimation method using multi-mapped reads on breakpoints. Lecture Notes in Computer Science. 8542: 208-219, 2014.
  26. Oki T, Nishimura K, Kitaura J, Togami K, Maehara A, Izawa K, Sakaue-Sawano A, Niida A, Miyano S, Aburatani H, Kiyonari H, Miyawaki A, Kitamura T. A novel cell-cycle-indicator, mVenus-p27K(-), identifies quiescent cells and visualizes G0-G1 transition. Sci Rep. 4:4012, 2014.
  27. Park H, Niida, A, Miyano S, Imoto S. Sparse overlapping group lasso for integrative multi-omics analysis. J Comput Biol. In press.
  28. Park H, Shimamura T, Miyano S, Imoto S. Robust prediction of anti-cancer drug sensitivity and sensitivity-specific biomarker. PLoS One. 9(1): e108990, 2014.
  29. Saini H, Raicar G, Lal S, Dehzangi A, Lyons J, Paliwal KK, Imoto S, Miyano S, Sharma A. Genetic algorithm for an optimized weighted voting scheme incorporating k-separated bigram transition probabilities to improve protein fold recognition. Asia-Pacific World Congress on Computer Science and Engineering 2014. In press.
  30. Sakata-Yanagimoto M, Enami T, Yoshida K, Shiraishi Y, Ishii R, Miyake Y, Muto H, Tsuyama N, Sato-Otsubo A, Okuno Y, Sakata S, Kamada Y, Nakamoto-Matsubara R, Tran NB, Izutsu K, Sato Y, Ohta Y, Furuta J, Shimizu S, Komeno T, Sato Y, Ito T, Noguchi M, Noguchi E, Sanada M, Chiba K, Tanaka H, Suzukawa K, Nanmoku T, Hasegawa Y, Nureki O, Miyano S, Nakamura N, Takeuchi K, Ogawa S, Chiba S. Somatic RHOA mutation in angioimmunoblastic T cell lymphoma. Nat Genet. 46(2):171-175, 2014.
  31. Sato Y, Maekawa S, Ishii R, Sanada M, Morikawa T, Shiraishi Y, Yoshida K, Nagata Y, Sato-Otsubo A, Yoshizato T, Suzuki H, Shiozawa Y, Kataoka K, Kon A, Aoki K, Chiba K, Tanaka H, Kume H, Miyano S, Fukayama M, Nureki O, Homma Y, Ogawa S. Recurrent somatic mutations underlie corticotropin-independent Cushing's syndrome. Science. 344(6186):917-920, 2014.
  32. Sawada G, Takahashi Y, Niida A, Shimamura T, Kurashige J, Matsumura T, Ueo H, Uchi R, Takano Y, Ueda M, Hirata H, Sakimura S, Shinden Y, Eguchi H, Sudo T, Sugimachi K, Miyano S, Doki Y, Mori M, Mimori K. Loss of CDCP1 expression promotes invasiveness and poor prognosis in esophageal squamous cell carcinoma. Ann Surg Oncol. 21 Suppl 4:640-647, 2014.
  33. Seki M, Yoshida K, Shiraishi Y, Shimamura T, Sato Y, Nishimura R, Okuno Y, Chiba K, Tanaka H, Kato K, Kato M, Hanada R, Nomura Y, Park MJ, Ishida T, Oka A, Igarashi T, Miyano S, Hayashi Y, Ogawa S, Takita J. Biallelic DICER1 mutations in sporadic pleuropulmonary blastoma. Cancer Res. 74(10):2742-2749, 2014.
  34. Sharma A, Paliwal KK, Imoto S, Miyano S, Sharma V, Ananthanarayanan R. A feature selection method using fixed-point algorithm for DNA microarray gene expression data. International Journal of Knowledge Based and Intelligent Engineering Systems. 18(1): 55-59, 2014.
  35. Sharma A, Dehzangi A, Lyons J, Imoto S, Miyano S, Nakai K, Patil A. Evaluation of sequence features from intrinsically disordered regions for the estimation of protein function. PLoS One. 9(2):e89890, 2014.
  36. Sharma A, Paliwal KK, Imoto S, Miyano S. A feature selection method using improved regularized linear discriminant analysis. Mach Vis Appl. 25(3):775-786, 2014.
  37. Shen W, Clemente MJ, Hosono N, Yoshida K, Przychodzen B, Yoshizato T, Shiraishi Y, Miyano S, Ogawa S, Maciejewski JP, Makishima H. Deep sequencing reveals stepwise mutation acquisition in paroxysmal nocturnal hemoglobinuria. J Clin Invest. 124(10):4529-4538, 2014.
  38. Sugimachi K, Niida A, Yamamoto K, Shimamura T, Imoto S, Iinuma H, Shinden Y, Eguchi H, Sudo T, Watanabe M, Tanaka J, Kudo S, Hase K, Kusunoki M, Yamada K, Shimada Y, Sugihara K, Maehara Y, Miyano S, Mori M, Mimori K. Allelic Imbalance at an 8q24 Oncogenic SNP is involved in activating MYC in human colorectal cancer. Ann Surg Oncol. 21 Suppl 4:515-521, 2014.
  39. Sung W-K, Sadakane K, Shibuya T, Belorkar A, Pyrogova I. An O(m log m)-time algorithm for detecting superbubbles. In press.
  40. Tagawa K, Homma H., Saito A, Fujita K, Chen X, Imoto S, Oka T, Ito H, Motoki K, Yoshida C, Hatsuta H, Murayama S, Iwatsubo T, Miyano S, Okazawa H. Comprehensive phosphoproteome analysis unravels the core signaling network that initiates the earliest synapse pathology in preclinical Alzheimer’s disease brain. Hum Mol Genet. In press.
  41. Takahashi R, Nagayama S, Furu M, Kajita Y, Jin Y-H, Kato T, Imoto S, Sakai Y, Toguchida J. AFAP1L1, a novel associating partner with vinculin, modulates cellular morphology and motility, and promotes the progression of colorectal cancers, Cancer Medicine, 3(4), 759–774, 2014.
  42. Tokunaga H, Munakata K, Katayama K, Yamaguchi R, Imoto S, Miyano S, Watanabe K. Clinical data mining related to the Japanese kampo concept “hie” (oversensitivity to coldness) in men and pre- and postmenopausal women. Evid Based Complement Alternat Med. 2014:832824, 2014.
  43. Totoki Y, Yoshida A, Hosoda F, Nakamura H, Hama N, Ogura K, Yoshida A, Fujiwara T, Arai Y, Toguchida J, Tsuda H, Miyano S, Kawai A, Shibata T. Unique mutation portraits and frequent COL2A1 gene alteration in chondrosarcoma. Genome Res. 24(9):1411-1420, 2014.
  44. Usuyama N, Shiraishi Y, Sato Y, Kume H, Homma Y, Ogawa S, Miyano S, Imoto S. HapMuC: somatic mutation calling using heterozygous germline variants near candidate mutations. Bioinformatics. 30(23): 3302-3309, 2014.
  45. Wang R, Yoshida K, Toki T, Sawada T, Uechi T, Okuno Y, Sato-Otsubo A, Kudo K, Kamimaki I, Kanezaki R, Shiraishi Y, Chiba K, Tanaka H, Terui K, Sato T, Iribe Y, Ohga S, Kuramitsu M, Hamaguchi I, Ohara A, Hara J, Goi K, Matsubara K, Koike K, Ishiguro A, Okamoto Y, Watanabe K, Kanno H, Kojima S, Miyano S, Kenmochi N, Ogawa S, Ito E. Loss of function mutations in RPL27 and RPS27 identified by whole-exome sequencing in Diamond-Blackfan anaemia. Br J Haematol. In press.
  46. Yamaguchi K, Yamaguchi R, Takahashi N, Ikenoue T, Fujii T, Shinozaki M, Tsurita G, Hata K, Niida A, Imoto S, Miyano S, Nakamura Y, Furukawa Y. Overexpression of cohesion establishment factor DSCC1 through E2F in colorectal cancer. PLoS One. 9(1):e85750, 2014.
  47. Yoshino T, Katayama K, Munakata K, Horiba Y, Yamaguchi R, Imoto S, Miyano S, Watanabe K. Kampo traditional pattern diagnosis and the clustering analysis of patients with cold sensation. J Altern Complement Med. 20(5):A47, 2014.

2013

  1. Affara M, Sanders D, Araki H, Tamada Y, Dunmore B, Humphreys S, Imoto S, Savoie C, Miyano S, Kuhara S, Print C, Charnock-Jones DS. Vasohibin-1 is identified as a master-regulator of endothelial cell apoptosis using gene network analysis. BMC Genomics. 14(1):23, 2013.
  2. Damm F, Chesnais V, Nagata Y, Yoshida K, Scourzic L, Okuno Y, Itzykson R, Sanada M, Shiraishi Y, Gelsi-Boyer V, Renneville A, Miyano S, Mori H, Shih LY, Park S, Dreyfus F, Guerci-Bresler A, Solary E, Rose C, Cheze S, Prébet T, Vey N, Legentil M, Duffourd Y, de Botton S, Preudhomme C, Birnbaum D, Bernard OA, Ogawa S, Fontenay M, Kosmider O. Blood. 122(18):3169-77, 2013.
  3. Furuta M, Kozaki K, Tanimoto K, Tanaka S, Arii S, Shimamura T, Niida A, Miyano S, Inazawa J. The tumor-suppressive miR-497-195 cluster targets multiple cell-cycle regulators in hepatocellular carcinoma. PLoS One. 8(3):e60155, 2013.
  4. Gómez-Seguí I, Makishima H, Jerez A, Yoshida K, Przychodzen B, Miyano S, Shiraishi Y, Husseinzadeh HD, Guinta K, Clemente M, Hosono N, McDevitt MA, Moliterno AR, Sekeres MA, Ogawa S, Maciejewski JP.　 Novel recurrent mutations in the RAS-like GTP-binding gene RIT1 in myeloid malignancies.　Leukemia. 27(9):1943-1946, 2013
  5. Hira A, Yabe H, Yoshida K, Okuno Y, Shiraishi Y, Chiba K, Tanaka H, Miyano S, Nakamura J, Kojima S, Ogawa S, Matsuo K, Takata M, Yabe M. Variant ALDH2 is associated with accelerated progression of bone marrow failure in Japanese Fanconi anemia patients. Blood. 122(18):3206-3209, 2013.
  6. Ishikawa T, Shimizu T, Ueki A, Yamaguchi SI, Onishi N, Sugihara E, Kuninaka S, Miyamoto T, Morioka H, Nakayama R, Kobayashi E, Toyama Y, Mabuchi Y, Matsuzaki Y, Yamaguchi R, Miyano S, Saya H. Twist2 functions as a tumor suppressor in murine osteosarcoma cells. Cancer Sci. 104(7):880-888, 2013.
  7. Katayama K, Yoshino T, Munakata K, Yamaguchi R, Imoto S, Miyano S, Watanabe K. Prescription of kampo drugs in the Japanese health care insurance program. Evid Based Complement Alternat Med. 2013:576973, 2013.
  8. Kayano M, Imoto S, Yamaguchi R, Miyano S. Multi-omics approach for estimating metabolic networks using low-order partial correlations. J Comput Biol. 20(8):571-582, 2013.
  9. Kitamura K, Yoshida K, Shiraishi Y, Chiba K, Tanaka H, Furukawa K, Miyano S, Ogawa S, Kunishima S. Normal neutrophil myosin IIA localization in an immunofluorescence analysis can rule out MYH9 disorders. J Thromb Haemost. 11(11):2071-2073, 2013.
  10. Komatsu M, Yoshimaru T, Matsuo T, Kiyotani K, Miyoshi Y, Tanahashi T, Rokutan K, Yamaguchi R, Saito A, Imoto S, Miyano S, Nakamura Y, Sasa M, Shimada M, Katagiri T. Molecular features of triple negative breast cancer cells by genome-wide gene expression profiling analysis. Int J Oncol. 42(2):478-506, 2013.
  11. Kon A, Shih LY, Minamino M, Sanada M, Shiraishi Y, Nagata Y, Yoshida K, Okuno Y, Bando M, Nakato R, Ishikawa S, Sato-Otsubo A, Nagae G, Nishimoto A, Haferlach C, Nowak D, Sato Y, Alpermann T, Nagasaki M, Shimamura T, Tanaka H, Chiba K, Yamamoto R, Yamaguchi T, Otsu M, Obara N, Sakata-Yanagimoto M, Nakamaki T, Ishiyama K, Nolte F, Hofmann WK, Miyawaki S, Chiba S, Mori H, Nakauchi H, Koeffler HP, Aburatani H, Haferlach T, Shirahige K, Miyano S, Ogawa S. Recurrent mutations in multiple components of the cohesin complex in myeloid neoplasms. Nat Genet. 45(10):1232-1237, 2013.
  12. Kunishima S, Okuno Y, Yoshida K, Shiraishi Y, Sanada M, Muramatsu H, Chiba K, Tanaka H, Miyazaki K, Sakai M, Ohtake M, Kobayashi R, Iguchi A, Takahashi Y, Miyano S, Saito H, Kojima S, Ogawa S. ACTN1 mutations cause congenital macrothrombocytopenia. American Journal of Human Genetics. 92(3):431-438, 2013.
  13. Makishima H, Yoshida K, Nguyen N, Przychodzen B, Sanada M, Okuno Y, Ng KP, Gudmundsson KO, Vishwakarma BA, Jerez A, Gomez-Segui I, Takahashi M, Shiraishi Y, Nagata Y, Guinta K, Mori H, Sekeres MA, Chiba K, Tanaka H, Muramatsu H, Sakaguchi H, Paquette RL, McDevitt MA, Kojima S, Saunthararajah Y, Miyano S, Shih LY, Du Y, Ogawa S, Maciejewski JP. Somatic SETBP1 mutations in myeloid malignancies. Nat Genet. 45(8):942-946, 2013.
  14. Niida A, Tremmel G, Imoto S, Miyano S. Multilayer cluster heat map visualizing biological tensor data. Lecture Notes in Bioinformatics. 8213: 116-125, 2013.
  15. Nagasaki M, Fujita A, Sekiya Y, Saito A, Ikeda E, Li C, Miyano S. XiP: a computational environment to create, extend and share workflows. Bioinformatics. 29(1):137-139, 2013.
  16. Onodera T, Sadakane K, Shibuya T. Detecting superbubbles in assembly graphs. Lecture Notes in Computer Science. 8126:338-348, 2013.
  17. Onodera T, Shibuya T. The gapped spectrum kernel for support vector machines. Lecture Notes in Computer Science. 7988:1-15, 2013.
  18. Osawa T, Tsuchida R, Muramatsu M, Shimamura T, Wang F, Suehiro JI, Kanki Y, Wada Y, Yuasa Y, Aburatani H, Miyano S, Minami T, Kodama T, Shibuya M. Inhibition of histone demethylase JMJD1A improves anti-angiogenic therapy and reduces tumor associated macrophages. Cancer Res. 73(10):3019-3028, 2013.
  19. Saida S, Watanabe K, Sato-Otsubo A, Terui K, Yoshida K, Okuno Y, Toki T, Wang RN, Shiraishi Y, Miyano S, Kato I, Morishima T, Fujino H, Umeda K, Hiramatsu H, Adachi S, Ito E, Ogawa S, Ito M, Nakahata T, Heike T. Clonal selection in xenografted TAM recapitulates the evolutionary process of myeloid leukemia in Down syndrome. Blood. 121(21):4377-4387, 2013.
  20. Saito MM, Imoto S, Yamaguchi R, Tsubokura M, Kami M, Nakada H, Sato H, Miyano S, Higuchi T. Enhancement of collective immunity in Tokyo Metropolitan area by selective vaccination against an emerging influenza pandemic. PLoS One. 8(9):e72866. 2013.
  21. Saito MM, Imoto S, Yamaguchi R, Sato H, Nakada H, Kami M, Miyano S, Higuchi T. Extension and verification of the SEIR model on the 2009 influenza A (H1N1) pandemic in Japan. Math Biosci. 246(1):47-54, 2013.
  22. Sakaguchi H, Okuno Y, Muramatsu H, Yoshida K, Shiraishi Y, Takahashi M, Kon A, Sanada M, Chiba K, Tanaka H, Makishima H, Wang X, Xu Y, Doisaki S, Hama A, Nakanishi K, Takahashi Y, Yoshida N, Maciejewski JP, Miyano S, Ogawa S, Kojima S. Exome sequencing identifies secondary mutations of SETBP1 and JAK3 in juvenile myelomonocytic leukemia. Nat Genet. 45(8):937-941, 2013.
  23. Sato Y, Yoshizato T, Shiraishi Y, Maekawa S, Okuno Y, Kamura T, Shimamura T, Sato-Otsubo A, Nagae G, Suzuki H, Nagata Y, Yoshida K, Kon A, Suzuki Y, Chiba K, Tanaka H, Niida A, Fujimoto A, Tsunoda T, Morikawa T, Maeda D, Kume H, Sugano S, Fukayama M, Aburatani H, Sanada M, Miyano S, Homma Y, Ogawa S. Integrated molecular analysis of clear-cell renal cell carcinoma. Nat Genet. 45(8):860-867, 2013.
  24. Sharma A, Paliwal KK, Dehzangi A, Lyons J, Imoto S, Miyano S. A strategy to select suitable physicochemical attributes of amino acids for protein fold recognition. BMC Bioinformatics. 14(1):233, 2013.
  25. Sharma A, Paliwal KK, Imoto S, Miyano S. Principal component analysis using QR decomposition. International Journal of Machine Learning and Cybernetics. 4(6): 679-683, 2013.
  26. Shiraishi Y, Sato Y, Chiba K, Okuno Y, Nagata Y, Yoshida K, Shiba N, Hayashi Y, Kume H, Homma Y, Sanada M, Ogawa S, Miyano S. An empirical Bayesian framework for somatic mutation detection from cancer. Nucleic Acids Res. 41(7): e89, 2013.
  27. Takatsuno Y, Mimori K, Yamamoto K, Sato T, Niida A, Inoue H, Imoto S, Kawano S, Yamaguchi R, Toh H, Iinuma H, Ishimaru S, Ishii H, Suzuki S, Tokudome S, Watanabe M, Tanaka JI, Kudo SE, Mochizuki H, Kusunoki M, Yamada K, Shimada Y, Moriya Y, Miyano S, Sugihara K, Mori M. The rs6983267 SNP is associated with MYC transcription efficiency, which promotes progression and worsens prognosis of colorectal cancer. Ann Surg Oncol. 20(4):1395-1402, 2013.
  28. Tamura T, Sone M, Nakamura Y, Shimamura T, Imoto S, Miyano S, Okazawa H. A restricted level of PQBP1 is needed for the best longevity of Drosophila. Neurobiol Aging. 34(1):356.e11-20. 2013. doi: 10.1016/j.neurobiolaging.2012.07.015.
  29. Yamaguchi R, Imoto S, Kami M, Watanabe K, Miyano S, Yuji K. Does Twitter trigger bursts in signature collections? PLoS One. 8(3):e58252, 2013.
  30. Yokobori T, Iinuma H, Shimamura T, Imoto S, Sugimachi K, Ishii H, Iwatsuki M, Ota D, Ohkuma M, Iwaya T, Nishida N, Kogo R, Sudo T, Tanaka F, Shibata K, Toh H, Sato T, Barnard GF, Fukagawa T, Yamamoto S, Nakanishi H, Sasaki S, Miyano S, Watanabe T, Kuwano H, Mimori K, Pantel K, Mori M. Plastin3 is a novel marker for circulating tumor cells undergoing the epithelial-mesenchymal transition and is associated with colorectal cancer prognosis. Cancer Res. 73(7):2059-2069, 2013.
  31. Yoshida K, Toki T, Okuno Y, Kanezaki R, Shiraishi Y, Sato-Otsubo A, Sanada M, Park MJ, Terui K, Suzuki H, Kon A, Nagata Y, Sato Y, Wang R, Shiba N, Chiba K, Tanaka H, Hama A, Muramatsu H, Hasegawa D, Nakamura K, Kanegane H, Tsukamoto K, Adachi S, Kawakami K, Kato K, Nishimura R, Izraeli S, Hayashi Y, Miyano S, Kojima S, Ito E, Ogawa S. The landscape of somatic mutations in Down syndrome-related myeloid disorders. Nat Genet. 45:1293–1299, 2013. Erratum in: Nat Genet. 45(12):1516, 2013.
  32. Yoshimaru T, Komatsu M, Matsuo T, Chen YA, Murakami Y, Mizuguchi K, Mizohata E, Inoue T, Akiyama M, Yamaguchi R, Imoto S, Miyano S, Miyoshi Y, Sasa M, Nakamura Y, Katagiri T. Targeting BIG3-PHB2 interaction to overcome tamoxifen resistance in breast cancer cells. Nat Commun. 4:2443, 2013.
  33. Yoshino T, Katayama K, Munakata K, Horiba Y, Yamaguchi R, Imoto S, Miyano S, Watanabe K. Statistical analysis of hie (cold sensation) and hiesho (cold disorder) in kampo clinic. Evid Based Complement Alternat Med. 2013:398458 (8 pages), 2013.

2012

  1. Bowe A, Onoder T, Sadakane K, Shibuya T. Succinct de Bruijn graphs. The 12th Workshop on Algorithms in Bioinformatics, Lecture Notes in Computer Science. 7534: 225-235, 2012.
  2. Fujimori S, Hino K, Saito A, Miyano S, Miyamoto-Sato E. PRD: A protein-RNA interaction database. Bioinformation. 8(15):729-730, 2012.
  3. Fujimori S, Hirai N, Masuoka K, Oshikubo T, Yamashita T, Washio T, Saito A, Nagasaki M, Miyano S, Miyamoto-Sato E. IRView: a database and viewer for protein interacting regions. Bioinformatics. 28(14):1949-1950, 2012.
  4. Fujimoto A, Totoki Y, Abe T, Boroevich KA, Hosoda F, Hai Nguyen H, Aoki M, Hosono N, Kubo M, Miya F, Arai Y, Takahashi H, Shirakihara T, Nagasaki M, Shibuya T, Nakano K, Watanabe-Makino K, Tanaka H, Nakamura H, Kusuda J, Ojima H, Shimada K, Okusaka T, Ueno M, Shigekawa Y, Kawakami Y, Arihiro K, Ohdan H, Gotoh K, Ishikawa O, Ariizumi S, Yamamoto M, Yamada T, Chayama K, Kosuge T, Yamaue H, Kamatani N, Miyano S, Nakagama H, Nakamura Y, Tsunoda T, Shibata T, Nakagawa H. Whole-genome sequencing of liver cancers identifies etiological influences on mutation patterns and recurrent mutations in chromatin regulators. Nature Genetics. 44(7):760-764, 2012.
  5. Fujita A, Severino P, Kojima K, Sato JR, Patriota AG, Miyano S. Functional clustering of time series gene expression data by Granger causality. BMC Systems Biology. 6:137, 2012.
  6. Hurley D, Araki H, Tamada Y, Dunmore B, Sanders D, Humphreys S, Affara M, Imoto S, Yasuda K, Tomiyasu Y, Tashiro K, Savoie C, Cho V, Smith S, Kuhara S, Miyano S, Charnock-Jones DS, Crampin EJ, Print CG. Gene network inference and visualization tools for biologists: application to new human transcriptome datasets. Nucleic Acids Res. 40(6):2377-2398, 2012. doi: 10.1093/nar/gkr902.
  7. Ishimaru S, Mimori K, Yamamoto K, Inoue H, Imoto S, Kawano S, Yamaguchi R, Sato T, Toh H, Iinuma H, Maeda T, Ishii H, Suzuki S, Tokudome S, Watanabe M, Tanaka JI, Kudo SE, Sugihara KI, Hase K, Mochizuki H, Kusunoki M, Yamada K, Shimada Y, Moriya Y, Barnard GF, Miyano S, Mori M. Increased risk for CRC in diabetic patients with the nonrisk allele of SNPs at 8q24. Ann Surg Oncol. 19(9):2853-2858, 2012.
  8. Kawano S, Shimamura T, Niida A, Imoto S, Yamaguchi R, Nagasaki M, Yoshida R, Print C, Miyano S. Identifying Gene pathways associated with cancer characteristics via sparse statistical methods. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9(4): 966-972, 2012.
  9. Katayama K, Yamaguchi R, Imoto S, Matsuura K, Watanabe K, Miyano S. Analysis of questionnaire for Traditional Medical and develop decision support system. Proc. 2012 International Workshop on Biomedical and Health Informatics. IEEE Computer Society Press. 762-763, 2012.
  10. Katayama K, Yamaguchi R, Imoto S, Matsuura K, Watanabe K, Miyano S. Symbolic hierarchical clustering for pain vector. Intelligent Decision Technologies. 16: 17-124, 2012.
  11. Katayama K, Yamaguchi R, Imoto S, Matsuura K, Watanabe K, Miyano S. Connection between traditional medicine and disease. ACM SIGHIT Record. 2 (1): 21-21. 2012.
  12. Kojima K, Imoto S, Yamaguchi R, Fujita A, Yamauchi M, Gotoh N, Miyano S. Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing. BMC Genomics. 13(Suppl 1):S6, 2012.
  13. Mimura I, Nangaku M, Kanki Y, Tsutsumi S, Inoue T, Kohro T, Yamamoto S, Fujita T, Shimamura T, Suehiro J, Taguchi A, Kobayashi M, Tanimura K, Inagaki T, Tanaka T, Hamakubo T, Sakai J, Aburatani H, Kodama T, Wada Y. Dynamic change of chromatin conformation in response to hypoxia enhances the expression of GLUT3 (SLC2A3) by cooperative interaction of hypoxia-inducible factor 1 and KDM3A. Mol Cell Biol. 32(15):3018-32, 2012.
  14. Niida A, Imoto S, Shimamura T, Miyano S. Statistical model-based testing to evaluate the recurrence of genomic aberrations. Bioinformatics. 28(12):i115-i120, 2012.
  15. Ogami K, Yamaguchi R, Imoto S, Tamada Y, Araki H, Print C, Miyano S. Computational gene network analysis reveals TNF-induced angiogenesis. BMC Systems Biology. 6 (Suppl 2):S12, 2012.
  16. Okayama H, Kohno T, Ishii1 Y, Shimada Y, Shiraishi K, Iwakawa R, Furuta K, Tsuta K, Shibata T, Yamamoto S, Watanabe S, Sakamoto H, Kumamoto K, Takenoshita S, Gotoh N, Mizuno H, Sarai A, Kawano S, Yamaguchi R, Miyano S, Yokota J. Identification of genes up-regulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res. 72(1):100-111, 2012.
  17. Onuki R, Yamada R, Yamaguchi R, Kanehisa M, Shibuya T. Population model-based inter-diplotype similarity measure for accurate diplotype clustering. J Comp Biol. 19(1): 55-67, 2012.
  18. Sahli M, Shibuya T. Arapan-S: A Fast and Highly Accurate Whole-Genome Assembly Software for Viruses and Small Genomes. BMC Research Notes. 5:243, 2012.
  19. Sahli M, Shibuya T. Max-Shift BM and Max-Shift Horspool: practical fast exact string matching algorithms. J Information Processing. 20(2): 419-425, 2012.
  20. Sahli M, Shibuya T. Argan - an artificial sequencing tool for simulated data and experimental. Proc. The 4th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2012). 29: 196-199, 2012.
  21. Sahli M, Shibuya T. An algorithm for classifying DNA reads. Proc. International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2012). 31: 59-63, 2012.
  22. Sahli M, Shibuya T. Qamar - A more accurate DNA sequencing error correcting algorithm. Proc. International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2012). 31: 53-58, 2012.
  23. Saito MM, Imoto S, Yamaguchi R, Miyano S, Higuchi T.　Identifiability of local transmissibility parameters in agent-based pandemic simulation. Proc. 15th International Conference on Information Fusion. IEEE Computer Society Press. 2466-2471, 2012.
  24. Saito MM, Imoto S, Yamaguchi R, Miyano S, Higuchi T. Parallel agent-based simulator for influenza pandemic. Lecture Notes in Computer Science. 7068: 361-370, 2012.
  25. Sharma A, Imoto S, Miyano S. A filter based feature selection algorithm using null space of covariance matrix for DNA microarray gene expression data. Current Bioinformatics. 7 (3): 289-294, 2012.
  26. Sharma A, Imoto S, Miyano S. A between-class overlapping filter-based method for transcriptome data analysis. J Bioinformatics and Computational Biology. 10(5):1250010, 2012.
  27. Sharma A, Imoto S, Miyano S. A top-r feature selection algorithm for microarray gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9(3): 754-64, 2012.
  28. Sharma A, Imoto S, Miyano S, Sharma V. Null space based feature selection method for gene expression data, International Journal of Machine Learning and Cybernetics. 3(4): 269-276, 2012.
  29. Terashi G, Shibuya T, Takeda-Shitaka M. LB3D: a protein 3D substructure search program based on the lower bound of a RMSD value. J Comp Biol. 19(5): 493-503, 2012.
  30. Wang L, Hurley D, Watkins W, Araki1 H, Tamada Y, Muthukaruppan A, Ranjard L, Derkac E, Imoto S, Miyano S, Crampin E, Print C. Cell cycle gene networks are associated with melanoma prognosis. PLoS One. 7(4): e34247, 2012.
  31. Yamamoto M, Yamaguchi R, Muanakata K, Takashima K, Nishiyama M, Hioki K, Ohnishi Y, Nagasaki M, Imoto S, Miyano S, Ishige A, Watanabe K. A microarray analysis of gnotobiotic mice indicating that microbial exposure during the neonatal period plays an essential role in immune system development. BMC Genomics. 13:335, 2012.
  32. Yamauchi M, Yamaguchi R, Nakata A, Kohno T, Nagasaki M, Shimamura T, Imoto S, Saito A, Ueno K, Hatanaka Y, Yoshida R, Higuchi T, Nomura M, Beer DG, Yokota J, Miyano S, Gotoh N. Epidermal growth factor receptor tyrosine kinase defines critical prognostic genes of stage I lung adenocarcinoma. PLoS One. 7(9): e43923, 2012.
  33. Yasuda T, Suzuki S, Nagasaki M, Miyano S. ChopSticks: High-resolution analysis of homozygous deletions by exploiting concordant read pairs. BMC Bioinformatics. 13(1):279, 2012.
  34. Yuji K, Imoto S, Yamaguchi R, Matsumura T, Murashige N, Kodama Y, Miyano S, Imai K, Kami M. Forecasting Japan's physician shortage in 2035 as the first full-fledged aged society. PLoS One. 7(11): e50410, 2012.

2011

  1. Chalkidis G, Nagasaki M, Miyano S. High performance hybrid functional Petri net simulations of biological pathway models on CUDA. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 8 (6): 1545-1556, 2011.
  2. Fujita A, Sato JR, Demas MAA, Yamaguchi R, Shimamura T, Ferreira CE, Sogayar, MC, Miyano S. Inferring contagion in regulatory networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 8(2):570-576, 2011.
  3. Furu M, Kajita Y, Nagayama S, Ishibe T, Shima Y, Nishijo K, Uejima D, Takahashi R, Aoyama T, Nakayama T, Nakamura T, Nakashima Y, Ikegawa M, Imoto S, Katagiri T, Nakamura Y, Toguchida J. Toguchida. Identification of AFAP1L1 as a prognostic marker for spindle cell sarcomas. Oncogene. 30(38): 4015-4025, 2011.
  4. Hasegawa T, Yamaguchi R, Nagasaki M, Imoto S, Miyano S. Comprehensive pharmacogenomic pathway screening by data assimilation. Lecture Notes in Bioinformatics. 6674: 160-171, 2011.
  5. Hurley D, Araki H, Tamada Y, Dunmore B, Sanders D, Humphreys S, Affara M, Imoto S, Yasuda K, Tomiyasu Y, Tashiro K, Savoie C, Cho V, Smith S, Kuhara S, Miyano S, Charnock-Jones DS, Crampin EJ, Print CG. Gene network inference and visualization tools for biologists: application to new human transcriptome datasets. Nucleic Acids Res. 2011 Dec 6. [Epub ahead of print]
  6. Imoto S, Kojima K, Perrier E, Tamada Y, Miyano S. Searching optimal Bayesian network structure on constraint search space: super-structure approach. Lecture Notes in Computer Science. 6797: 210-218, 2011.
  7. Jeong E, Nagasaki M, Ueno K, Miyano S. Ontology-based instance data validation for high-quality curated biological pathways. BMC Bioinformatics. 12(Suppl 1): S8, 2011.
  8. Jeong E, Nagasaki M, Ikeda E, Saito A, Miyano S. CSO validator: improving manual curation workflow for biological pathways. Bioinformatics. 27(17): 2471-2472, 2011.
  9. Katayama K, Yamaguchi R, Imoto S, Tokunaga H, Imazu Y, Matuura K, K. Watanabe, Miyano S. Symbolic hierarchical clustering for visual analogue scale data. Smart Innovation, Systems and Technologies. 10: 799-805, 2011.
  10. Katayama K, Yamaguchi R, Imoto S, Matsuura K, Watanabe K, Miyano S. Clustering for visual analogue scale data in symbolic data analysis. Procedia Computer Science. 6: 370-374, 2011.
  11. Kimura D, Kuboyama T, Shibuya T, Kashima H. A subpath kernel for rooted unordered trees. Proc. The 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2011), 62-74, 2011.
  12. Kogo R, Shimamura T, Mimori K, Kawahara K, Imoto S, Sudo T, Tanaka F, Shibata K, Suzuki A, Komune S, Miyano S, Mori M. Long non-coding RNA HOTAIR regulates Polycomb-dependent chromatin modification and is associated with poor prognosis in colorectal cancers. Cancer Res. 2011 Aug 23. [Epub ahead of print]
  13. Koh CH, Nagasaki M, Saito A, Li C, Wong L, Miyano S. MIRACH: Efficient model checker for quantitative biological pathway models. Bioinformatics. 27(5): 734-735, 2011.
  14. Li C, Kuroyanagi K, Nagasaki M, Miyano S, Parameter estimation of biological pathways using data assimilation and model checking, Proceedings of the 2nd International Workshop on Biological Processes & Petri Nets (BioPPN2011). EURO-WS. 724: 53-70, 2011. Link
  15. Li C, Nagasaki M, Miyano S. Online model checking approach based parameter estimation to a neuronal fate decision simulation model in Caenorhabditis elegans with hybrid functional Petri net with extension. Molecular BioSystems. 7(5):1576-1592, 2011.
  16. Matsuno H, Nagasaki M, Miyano S. Hybrid Petri net based modeling for biological pathway simulation. Natural Computing 10(3): 1099-1120, 2011.
  17. Nagasaki M, Saito A, Fujita A, Tremmel G, Ueno K, Ikeda E, Jeong E, Miyano S. Systems biology model repository for macrophage pathway simulation. Bioinformatics. 27 (11): 1591-1593, 2011.
  18. Onodera T, Shibuya T. An index structure for spaced seed search. Lecture Notes in Computer Science. 7074: 764-772, 2011.
  19. Osoda T, Miyano S. 2D-Qsar for 450 types of amino acid induction peptides with a novel substructure pair descriptor having wider scope. J Cheminform. 3(1): 50, 2011.
  20. Osoda T, Miyano S. Noise-tolerant active learning algorithm. Proc. The 2011 International Conference on Data Mining. 10-14, 2011.
  21. Saito MM, Imoto S, Yamaguchi R, Miyano S, Higuchi T. Parallel agent-based simulator for influenza pandemic. Lecture Notes in Computer Science. 7068: 361-370, 2011.
  22. Saito MM, Imoto S, Yamaguchi R, Miyano S, Higuchi T. Estimation of macroscopic parameter in agent-based pandemic simulation. Proc. 13th International Conference on Information Fusion, 1-6, 2011.
  23. Sharma A, Imoto S, Miyano S. A top-r feature selection algorithm for microarray gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2011 Nov 11. [Epub ahead of print]
  24. Sharma A, Koh CH, Imoto S, Miyano S. Strategy of finding optimal number of features on gene expression data. Electronic Letters. 47(8): 480-482, 2011.
  25. Shimamura T, Imoto S, Shimada Y, Hosono Y, Niida A, Nagasaki M, Yamaguchi R, Takahashi T, Miyano S. A novel network profiling analysis reveals system changes in epithelial-mesenchymal transition. PLoS ONE. 6(6): e20804, 2011.
  26. Shiraishi U, Okada-Hatakeyama M, Miyano S. A rank-based statistical test for measuring synergistic effects between two gene sets. Bioinformatics. 27 (17): 2399-2405, 2011.
  27. Sogawa Y, Shimizu S, Shimamura T, Hyvarinen A, Washio T, Imoto S. Estimating exogenous variables in data with more variables than observations. Neural Networks. 24(8): 875-880, 2011.
  28. Suzuki S, Yasuda T, Shiraishi Y, Miyano S, Nagasaki M. ClipCrop: a tool for detecting structural variations with single-base resolution using soft-clipping information. BMC Bioinformatics. 12:S7, 2011.
  29. Tamada Y, Imoto S, Miyano S. Parallel algorithm for learning optimal Bayesian network structure. J Machine Learning Research. 12: 2437-2459, 2011. Link
  30. Tamada Y, Imoto S, Araki H, Nagasaki M, Print C, Charnock-Jones DS, Miyano S. Estimating genome-wide gene networks using nonparametric Bayesian network models on massively parallel computers. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 8(3): 683 - 697, 2011.
  31. Tamada Y, Shimamura T, Yamaguchi R, Imoto S, Nagasaki M, Miyano S. SiGN: large-scale gene network estimation environment for high performance computing. Genome Informatics. 25: 40-52, 2011. Link
  32. Tamada Y, Yamaguchi R, Imoto S, Hirose O, Yoshida R, Nagasaki M, Miyano S. SiGN-SSM: open source parallel software for estimating gene networks with state space models. Bioinformatics. 27: 1172-1173, 2011.
  33. Yamaguchi K, Sakai M, Kim J, Tsunesumi SI, Fujii T, Ikenoue T, Yamada Y, Akiyama Y, Muto Y, Yamaguchi R, Miyano S, Nakamura Y, Furukawa Y. MRG-binding protein contributes to development of colorectal cancer. Cancer Sci. 102 (8): 1486-1492, 2011.
  34. Yamauchi M, Yoshino I, Yamaguchi R, Shimamura T, Nagasaki M, Imoto S, Niida A, Koizumi F, Kohno T, Yokota J, Miyano S, Gotoh N. N-cadherin expression is a potential survival mechanism of gefitinib-resistant lung cancer cells. Am J Cancer Res. 1(7): 823-833, 2011.
  35. Yoshida K, Sanada M, Shiraishi Y, Nowak D, Nagata Y, Yamamoto R, Sato Y, Sato-Otsubo A, Kon A, Nagasaki M, Chalkidis G, Suzuki Y, Shiosaka M, Kawahata R, Yamaguchi T, Otsu M, Obara N, Sakata-Yanagimoto M, Ishiyama K, Mori H, Nolte F, Hofmann WK, Miyawaki S, Sugano S, Haferlach C, Koeffler HP, Shih LY, Haferlach T, Chiba S, Nakauchi H, Miyano S, Ogawa S.　 Frequent pathway mutations of splicing machinery in myelodysplasia. Nature. 478(7367): 64-69, 2011.

2010

  1. Do JH, Nagasaki M, Miyano S, The systems approach to the prespore-specific activation of sigma factor SigF in Bacillus subtilis. Biosystems. 100: 178-184, 2010.
  2. Fujimoto A, Nakagawa H, Hosono N, Nakano K, Abe G, Boroevich KA, Nagasaki M, Yamaguchi R, Shibuya T, Kubo M, Miyano S, Nakamura Y, Tsunoda T. Whole-genome sequencing and comprehensive variant analysis of a Japanese individual using massively parallel sequencing. Nature Genetics. 42: 931–936, 2010.
  3. Fujita A, Kojima K, Patriota AG, Sato JR, Severino P, Miyano S. A fast and robust statistical test based on likelihood ratio with Bartlett correction to identify Granger causality between gene sets. Bioinformatics. 26(18):2349-2351, 2010.
  4. Fujita A, Nagasaki M, Imoto S, Saito A, Ikeda E, Shimamura T, Yamaguchi R, Hayashizaki Y, Miyano S. Comparison of gene expression profiles produced by CAGE, illumina microarray and Real Time RT-PCR. Genome Informatics. 24: 56-68, 2010.
  5. Fujita A, Sato JR, Kojima K, Gomes LR, Sogayar MC, Miyano S. Identification of Granger causality between gene sets. J. Bioinformatics and Computational Biology. 8(4): 679–701, 2010.
  6. Fujita, A, Severino P, Sato JR, Miyano S. Granger causality in systems biology: modeling gene networks in time series microarray data using vector autoregressive models. Lecture Notes in Bioinformatics. 6268: 13-24, 2010.
  7. Higashigaki T, Kojima K, Yamaguchi R, Inoue M, Imoto S, Miyano S. Identifying hidden confounders in gene networks by Bayesian networks. Proc. 10th IEEE Bioinformatics and Bioengineering. 168-173, 2010.
  8. International Cancer Genome Consortium, Hudson TJ. et al. International network of cancer genome projects. Nature. 464(7291):993-998, 2010.
  9. Kaufmann K, Nagasaki M., Jáuregui, R. Modelling the molecular interactions in the flower developmental network of Arabidopsis thaliana. In Silico Biol. 10: 0008, 2010.
  10. Kawano S, Shimamura T, Niida A, Imoto S, Yamaguchi R, Nagasaki M, Yoshida R, Print C, Miyano S. Discovering functional gene pathways associated with cancer heterogeneity via sparse supervised learning.　 Proc. IEEE 10th International Symposium on Bioinformatics & Bioengineering. 253-258, 2010.
  11. Koh CH, Nagasaki M, Saito A, Wong L, Miyano S. DA 1.0: parameter estimation of biological pathways using data assimilation approach. Bioinformatics. 26(14):1794-1796, 2010.
  12. Kojima K, Imoto S, Nagasaki M, Miyano S. Gene regulatory network clustering for graph layout based on microarray gene expression data. Genome Informatics. 24: 84-95, 2010.
  13. Kojima K, Nagasaki M, Miyano S. An efficient biological pathway layout algorithm combining grid-layout and spring embedder for complicated cellular location information. BMC Bioinformatics. 11:335, 2010.
  14. Kojima K, Perrier E, Imoto S, Miyano S. Optimal search on clustered structural constraint for learning Bayesian network structure. J. Machine Learning Research.11: 285−310, 2010.
  15. Li C, Nagasaki M, Saito A, Miyano S. Time-dependent structural transformation analysis to high-level Petri net model with active state transition diagram. BMC Systems Biology. 4:39, 2010.
  16. Miwa Y, Li C, Ge Q-W, Matsuno H, Miyano S. On determining delay time of transitions for Petri net based signaling pathways by introducing stochastic decision rules. In Silico Biol. 10: 0004, 2010.
  17. Miwa Y, Murakami Y, Ge Q-W, Li C, Matsuno H, Miyano, S. Delay time determination for the timed Petri net model of a signaling pathway based on its structural information. IEICE Trans. Fundamentals of Electronics, Communications and Computer Sciences. E93-A(12), 2717-2729, 2010.
  18. Nagasaki M, Saito A, Jeong E, Li C, Kojima K, Ikeda E, Miyano S. Cell Illustrator 4.0: A computational platform for systems biology. In Silico Biol. 10: 0002, 2010.
  19. Niida A, Imoto S, Yamaguchi R, Nagasaki M, Miyano S. Gene set-based module discovery decodes cis-regulatory codes governing diverse gene expression across human multiple tissues. PLoS ONE. 5(6):e10910, 2010.
  20. Niida A, Imoto S, Yamaguchi R, Nagasaki M, Fujita A, Shimamura T, Miyano S. Model-free unsupervised gene set screening based on information enrichment in expression profiles. Bioinformatics. 26(24):3090-3097, 2010.
  21. Sato H, Nakada H, Yamaguchi R, Imoto S, Miyano S, Kami M. When should we intervene to control the 2009 influenza A(H1N1) pandemic? Euro Surveill. 7:15(1), pii: 19455, 2010.
  22. Shibuya T. Searching protein 3-D structures in faster than linear time. J Comput Biol. 17(4): 593-602, 2010.
  23. Shibuya T. Searching protein 3-D structures in linear time. J Comput Biol. 17(3): 203-219, 2010.
  24. Shibuya, T. Geometric suffix tree: Indexing protein 3-D structures. JACM. 57(3): 1-17, 2010.
  25. Shibuya T. Fast hinge detection algorithms for flexible protein structures. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 7(2): 333-341, 2010.
  26. Shibuya T, Jansson J, Sadakane. K., Linear-time protein 3-D structure searching with insertions and deletions. BMC Algorithms for Molecular Biology. 5:7, 2010.
  27. Shimamura T, Imoto S, Yamaguchi R, Nagasaki M, Miyano S. Inferring dynamic gene networks under varying conditions for transcriptomic network comparison. Bioinformatics. 26(8):1064-1072, 2010.
  28. Shimamura T, Imoto S, Nagasaki M, Yamauchi M, Yamaguchi R, Fujita A, Tamada Y, Gotoh N, Miyano S. Collocation-based sparse estimation for constructing dynamic gene networks. Genome Informatics. 24: 164-178, 2010.
  29. Sogawa Y, Shimizu S, Hyvarinen A, Washio T, Shimamura T, Imoto S. Discovery of exogenous variables in data with more variables than observations. Proc. 20th International Conference on Artificial Neural Networks. 67-76, 2010.
  30. Tasaki S, Nagasaki M, Kozuka-Hata H, Semba K, Gotoh N, Hattori S, Inoue J, Yamamoto T, Miyano S, Sugano S, Oyama M. Phosphoproteomics-based modeling defines the regulatory mechanism underlying aberrant EGFR signaling. PLoS ONE. 5(11): e13926, 2010.
  31. Yamaguchi R, Imoto S, Miyano S. Network-based predictions and simulations by biological state space models: Search for drug mode of action. J. Computer Science and Technology. 25(1): 131-153, 2010.
  32. Yuji K, Matsumura T, Miyano S, Tsuchiya R, Kami M. Human papillomavirus vaccine coverage. Lancet. 376(9738):329-330, 2010.

2009

  1. Araki, H.*, Tamada, Y.*, Imoto, S.*, Dunmore, B., Sanders, D., Humphrey, S., Nagasaki, M., Doi, A., Nakanishi, Y., Yasuda, K., Tomiyasu, Y., Tashiro, K., Print, C., Charnock-Jones, D.S., Kuhara, S., Miyano, S. Analysis of PPARalpha-dependent and PPARalpha-independent transcript regulation following fenofibrate treatment of human endothelial cells. Angiogenesis. 12(3): 221-229, 2009. (*Equally contributed).
  2. Do, J.H., Miyano, S., Choi, D.K., Statistical approaches to genome-wide biological networks. Biochip J. 3(3): 190-202, 2009.
  3. Do, J.H., Yamaguchi, R., Miyano, S. Exploring temporal transcription regulation structure of Aspergillus fumigatus in heat shock by state space model. BMC Genomics. 10:306, 2009.
  4. Fujita, A., Sato, J.R., Demas, M.A.A, Yamaguchi, R., Shimamura, T., Ferreira, C.E., Sogayar, M.C., Miyano, S., Inferring contagion in regulatory networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics (in press).
  5. Fujita, A., Patriota, A.G., Sato, J.R., Miyano, S., The impact of measurement error in the identification of regulatory networks. BMC Bioinformatics. 10:412, 2009.
  6. Fujita, A., Sato, J.R., da Silva, F.H.L., Galvao, M.C., Sogayar, M.C., Miyano, S., Quality control and reproducibility in DNA microarray experiments. Genome Informatics. 23:21-31, 2009.
  7. Fujita, A., Sato, J.R., Demas, M.A.A, Sogayar, M.C., Ferreira, C.E., Miyano, S. Comparing Pearson, Spearman and Hoeffding's D measure for gene expressionassociation analysis. J. Bioinformatics and Computational Biology. 7(4): 663-684, 2009.
  8. Hashimoto, T.B., Nagasaki, M., Kojima, K., Miyano, S. BFL: a node and edge betweenness based fast layout algorithm for large scale networks. BMC Bioinformatics. 10:19, 2009.
  9. Li, C.*, Nagasaki, M.*#, Ueno, K., Miyano, S. Simulation-based model checking approach to cell fate pecification during Caenorhabditis elegans vulval development by hybrid functional Petri net with extension. BMC Systems Biology. 3:42, 2009. doi:10.1186/1752-0509-3-42 (*: These authors equally contributed, #: Corresponding author).
  10. Miyakawa, C., Sugii, M., Matsuno, H., Miyano, S. Computational predictions for functional proteins working after cleaved in apoptotic pathway. Proc. Second International Workshop on Intelligent Informatics in Biology and Medicine, IEEE Computer Society Press, 807-812, 2009.
  11. Miyano, S., Yamaguchi, R., Tamada, Y., Nagasaki, M., Imoto, S. Gene networks viewed through two models. Lecture Notes in Computer Science. 5462: 54-66, 2009. (Proc. First International Conference on Bioinformatics and Computational Biology (BICoB)). doi: 10.1007/978-3-642-00727-9_8.
  12. Nakamura, K., Yoshida, R., Nagasaki, M., Miyano, S., Higuchi, T. Parameter estimation of in silico biological pathways with particle filtering towards a petascale computing. Pacific Symposium on Biocomputing. 14: 227-238, 2009.
  13. Shimamura, T., Imoto, S., Yamaguchi, R., Fujita, A., Nagasaki, M., Miyano, S. Recursive regularization for inferring gene networks from time-course gene expression profiles. BMC Systems Biology. 3:41, 2009.
  14. Tamada, Y., Araki, H., Imoto, S., Nagasaki, M., Doi, A., Nakanishi, Y., Tomiyasu, Y., Yasuda, K., Dunmore, B., Sanders, D., Humphreys, S., Print, C., Charnock-Jones, D.S., Tashiro, K., Kuhara, S., Miyano, S. Unraveling dynamic activities of autocrine pathways that control drug-response transcriptome networks. Pacific Symposium on Biocomputing. 14: 251–263, 2009.
  15. Watanabe-Fukuda, Y., Yamamoto, M., Miura, N., Fukutake, M., Ishige, A., Yamaguchi, R., Nagasaki, M., Saito, A., Imoto, S., Miyano, S., Takeda, J., Watanabe, K. Orengedokuto and berberine improve indomethacin-induced small intestinal injury via adenosine. Journal of Gastroenterology. 44(5):380-389, 2009.
  16. Yamamoto, T., Miyano, S., Nagasaki, M., Bannai, H. Better decomposition heuristics for the maximum-weight connected graph problem using betweenness centrality. Lecture Notes in Artificial Intelligence. 5808:465-473, 2009.
  17. Yashiro, Y., Bannai, H., Minowa, T., Yabiku, T., Miyano, S., Osawa, M., Iwama, A., Nakauchi, H. Transcriptional profiling of hematopoietic stem cells by high-throughput sequencing. I.J. Hematology. 89(1):24-33, 2009.
  18. Yoshikawa, N., Nagasaki, M., Sano, M., Tokudome, S., Ueno, K., Shimizu, N., Imoto, S., Miyano, S., Suematsu, M., Fukuda, K., Morimoto, C., Tanaka, H. Ligand-based gene expression profiling reveals novel roles of glucocorticoid receptor in cardiac metabolism. Am J Physiol Endocrinol Metab. 296(6):E1363-E1373, 2009.
  19. Kojima, K., Yamaguchi, R., Imoto, S., Yamauchi, M., Nagasaki, M., Yoshida, R., Shimamura, T., Ueno, K., Higuchi, T., Gotoh, N., Miyano, S. A state space representation of VAR models with sparse learning for dynamic gene networks. Genome Informatics. 22: 56-68, 2009.
  20. Niida, A., Imoto, S., Nagasaki, M., Yamaguchi, R., Miyano, S. A novel meta-analysis approach of cancer transcriptomes reveals prevailing transcriptional networks in cancer cells. Genome Informatics. 22: 121-131, 2009.

2008

  1. Ando, T., Konishi, S., Imoto, S. Nonlinear regression modeling via regularized radial basis function networks. Journal of Statistical Planning and Inference. 138(11): 3616-3633, 2008.
  2. Do, J.H., Miyano, S. The GC and window-averaged DNA curvature profile of secondary metabolite gene cluster in Aspergillus fumigatus genome. Applied Microbiology and Biotechnology. 80(5):841-847, 2008.
  3. Fujita, A., Gomes, L.R., Sato, J.R., Yamaguchi, R., Thomaz, C.E., Sogayar, M.C., Miyano, S. Multivariate gene expression analysis reveals functional connectivity changes between normal/tumoral prostates. BMC Systems Biology. 2:106, 2008.
  4. Fujita, A., Sato, J.R., Garay-Malpartida, H.M., Sogayar, M.C., Ferreira, C.E., Miyano, S. Modeling nonlinear gene regulatory networks from time series gene expression data. J. Bioinformatics and Computational Biology. 6(5): 961 – 979, 2008.
  5. Hatanaka, Y., Nagasaki, M., Yamaguchi, R., Obayashi, T., Numata, K., Fujita, A., Shimamura, T., Tamada, Y., Imoto, S., Kinoshita, K., Nakai, K., Miyano, S. A novel strategy to search conserved transcription factor binding sites among coexpressing genes in human. Genome Informatics. 20:212-221, 2008.
  6. Hirose, O., Yoshida, R., Imoto, S., Yamaguchi, R., Higuchi, T., Charnock-Jones, D.S., Print, C., Miyano, S. Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models. Bioinformatics. 24(7): 932-942, 2008.
  7. Hirose, O., Yoshida, R., Yamaguchi, R., Imoto, S., Higuchi, T., Miyano, S. Analyzing time course gene expression data with biological and technical replicates to estimate gene networks by state space models. Proc. 2nd Asia International Conference on Modelling & Simulation, 940-946, 2008. (AMS2008: Refereed conference)
  8. Jeong, E., Nagasaki, M., Miyano, S. Rule-based reasoning for system dynamics in cell systems. Genome Informatics. 20:25-36, 2008.
  9. Kitakaze, H., Kanda, M., Nakatsuka, H., Ikeda, N., Matsuno, H., Miyano, S. Prediction of fragile points for robustness checking of cell systems. IEICE TRANSACTIONS on Information and Systems D. J91-D(9):2404-2417, 2008.
  10. Kojima, K., Fujita, A., Shimamura, T., Imoto, S., Miyano, S. Estimation of nonlinear gene regulatory networks via L1 regularized NVAR from time series gene expression data. Genome Informatics. 20:37-51, 2008.
  11. Kojima, K., Nagasaki, M.*, Miyano, S. Fast grid layout algorithm for biological networks with sweep calculation. Bioinformatics. 24(12): 1426-1432, 2008 (*: Corresponding author)
  12. Mito, N., Ikegami, Y., Matsuno, H., Miyano, S., Inouye, S. Simulation analysis for the effect of light-dark cycle on the entrainment in circadian rhythm. Genome Informatics. 21:212-223, 2008.
  13. Nagasaki, M.*, Saito, A., Chen, L., Jeong, E., Miyano, S. Systematic reconstruction of TRANSPATH data into Cell System Markup Language. BMC Systems Biology. 2:53 (23Jun2008), 2008. (*: Corresponding author)
  14. Niida, A., Smith, A.D., Imoto, S., Tsutsumi, S., Aburatani, H., Zhang, M.Q., Akiyama, T. Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells. BMC Bioinformatics. 9:404, 2008.
  15. Numata, K., Yoshida, R., Nagasaki, M., Saito, S., Imoto, S., Miyano, S. ExonMiner: Web service for analysis of GeneChip exon array data. BMC Bioinformatics. 9:494, 2008.
  16. Numata, K., Imoto, S., Miyano, S. Partial order-based Bayesian network learning algorithm for estimating gene networks. Proc. IEEE 8th International Symposium on Bioinformatics & Bioengineering, IEEE Computer Society, 357-360, 2008. 　(BIBE2008: Refereed conference; Digital Object Identifier 10.1109/BIBM.2008.85)
  17. Perrier, E., Imoto, S., Miyano, S. Finding optimal Bayesian network given a super-structure. J. Machine Learning Research. 9: 2251-2286, 2008.
  18. Yamaguchi, R., Imoto, S., Yamauchi, M., Nagasaki, M., Yoshida, R., Shimamura, T., Hatanaka, Y., Ueno, K., Higuchi, T., Gotoh, N., Miyano, S. Predicting differences in gene regulatory systems by state space models. Genome Informatics. 21:101-113, 2008.
  19. Yoshida, R., Nagasaki, M., Yamaguchi, R., Imoto, S., Miyano, S., Higuchi, T. Bayesian learning of biological pathways on genomic data assimilation. Bioinformatics. 24(22):2592-2601, 2008.

2007

  1. Affara, M., Dunmore, B., Savoie, C.J., Imoto, S., Tamada, Y., Araki, H., Charnock-Jones, D.S., Miyano, S., Print, C. Understanding endothelial cell apoptosis: What can the transcriptome glycome and proteome reveal? Philosophical Transactions of Royal Society. 362(1484):1469-1487, 2007.
  2. Akutsu, T., Bannai, H., Miyano, S., Ott, S. On the complexity of deriving position specific score matrices from positive and negative sequences. Discrete Applied Mathematics.155: 676-685, 2007. (Extended abstract: Proceedings of 13th Annual Symposium on Combinatorial Pattern Matching (CPM 2002). Lecture Notes in Computer Science. 2373:168-177, 2002.)
  3. Fujita, A., Sato, J.R., Garay-Malpartida, H.M., Yamaguchi, R., Miyano, S., Sogayar, M.C., Ferreira, C.E.　Modeling gene expression regulatory networks with the sparse vector autoregressive model. BMC Systems Biology. 1:39, 2007. (doi:10.1186/1752-0509-1-39)
  4. Gupta, P.K., Yoshida, R., Imoto, S., Yamaguchi, R., Miyano, S. Statistical absolute evaluation of gene ontology terms with gene expression data. Lecture Notes in Bioinformatics. 4463:146-157, 2007.
  5. Hirose, O., Yoshida, R., Yamaguchi, R., Imoto, S., Higuchi, T., Miyano, S. Clustering with time course gene expression profiles and the mixture of state space models. Genome Informatics. 18:258-266, 2007.
  6. Imoto, S. Knowledge discovery of causal relations among genes from microarray gene expression data (in Japanese with English abstract). Journal of Japan Statistical Sciety. 37(1): 55-70, 2007.
  7. Imoto, S., Tamada, Y., Savoie, C.J., Miyano, S., Analysis of gene networks for drug target discovery and validation. Methods in Molecular Biology. 360:33-56, 2007. (Target Discovery and Validation, Volume 1, 33-56, 2007 (a volume of “Methods in Molecular Biology” series), Walker, J. and Sioud, M. (Eds.), Humana Press, USA.).
  8. Imoto, S., Miyano, S. Bayesian network approach to estimate gene networks. A. Mittal, A. Kassim and T. Tan (Eds.), Bayesian Network Technologies: Applications and Graphical Models, Idea Group Publishers, USA. 269-299, 2007. (Refereed book chapter).
  9. Jeong, E.*, Nagasaki, M.*, Miyano, S. Conversion from BioPAX to CSO for system dynamics and visualization of biological pathway. Genome Informatics. 18, 225-236, 2007. (* These authors equally contributed)
  10. Jeong, E.*, Nagasaki, M.*, Saito, A., Miyano, S. Cell System Ontology: Representation for modeling, visualizing, and simulating biological pathways. In Silico Biology 7, 0055, 2007. (* These authors equally contributed)
  11. Kojima, K.*, Nagasaki, M.*, Jeong, E., Kato, M., Miyano, S. An efficient grid layout algorithm for biological networks utilizing various biological attributes. BMC Bioinformatics 2007, 8:76 (6 March 2007). (* These authors equally contributed)
  12. Li, C., Ge, Q.-W., Nakata, M., Matsuno, H., Miyano, S. Modeling and simulation of signal transductions in an apoptosis pathway by using timed Petri nets. J. Biosciences. 32(1):113-125, 2007.
  13. Numata, K., Imoto, S., Miyano, S. A structure learning algorithm for inference of gene networks from microarray gene expression data using Bayesian networks. Proc. IEEE 7th International Symposium on Bioinformatics & Bioengineering, 1280-1284, 2007. (BIBE2007: Refereed conference; Digital Object Identifier 10.1109/BIBE.2007.4375731)
  14. Saito, A., Nagasaki, M., Oyama, M., Kozuka-Hata, H., Semba, K., Sugano, S., Yamamoto, T., Miyano, S. AYUMS: an algorithm for completely automatic quantitation based on LC-MS/MS proteome data and its application to the analysis of signal transduction. BMC Bioinformatics. 8:15, 2007.
  15. Shimamura, T., Yamaguchi, R., Imoto, S., Miyano, S. Weighted lasso in graphical Gaussian modeling for large gene network estimation based on microarray data. Genome Informatics. 19: 142-153, 2007.
  16. Sugii, M., Okada, R., Matsuno, H., Miyano, S. Performance improvement in protein N-myristoyl classification by BONSAI with insignificant indexing symbol. Genome Informatics. 18: 277-286, 2007.
  17. Termier, A., Tamada, Y., Numata, K., Imoto, S., Washio, T., Higuchi, T. DIGDAG, a first algorithm to mine closed frequent embedded sub-DAGs. Proc. 5th International Workshop on Mining and Learning with Graphs. CR-ROM, 2007. (MLG2007: Refereed conference). (Peer-reviewed conference paper)
  18. Yamaguchi, R., Yamamoto, M., Imoto, S., Nagasaki, M., Yoshida, R., Tsujii, K., Ishiga, A., Asou, H., Watanabe, K., Miyano, S. Identification of activated transcription factors from microarray gene expression data of Kampo-medicine treated mice. Genome Informatics. 18: 119-129, 2007.
  19. Yamaguchi, R., Yoshida, R., Imoto, S., Higuchi, T., Miyano, S. Finding module-based gene networks with state-space models? Mining high-dimensional and short time-course gene expression data. IEEE Signal Processing Magazine. 24(1): 37-46, 2007.
  20. Yoshida, R., Numata, K., Imoto, S., Nagasaki, M., Doi, A., Ueno, K., Miyano, S. Computational discovery of aberrant splice variations with genome-wide exon expression profiles. Proc. IEEE 7th International Symposium on Bioinformatics & Bioengineering, 715-722, 2007. (IEEE BIBE2007: Refereed conference; Digital Object Identifier 10.1109/BIBE.2007.4375639)

2006

  1. Doi, A., Nagasaki, M., Matsuno, H., Miyano, S. Simulation based validation of the p53 transcriptional activity with hybrid functional Petri net. In Silico Biology. 6(1-2): 1-13, 2006.
  2. Doi, A., Nagasaki, M., Ueno, K., Matsuno, H., Miyano, S. A combined pathway to simulate CDK-dependent phosphorylation and ARF-dependent stabilization for p53 transcriptional activity. Genome Informatics. 17(1): 112-123, 2006.
  3. Imoto, S., Tamada, Y., Araki, H., Yasuda, K., Print, C.G., Charnock-Jones, S.D., Sanders, D., Savoie, C.J., Tashiro, K., Kuhara, S., Miyano, S. Computational strategy for discovering druggable gene networks from genome-wide RNA expression profiles. Pacific Symposium on Biocomputing. 11: 559-571, 2006.
  4. Imoto, S., Higuchi, T., Goto, T., Miyano, S. Error tolerant model for incorporating biological knowledge with expression data in estimating gene networks. Statistical Methodology, 3(1):1-16, 2006.
  5. Jeong, E., Miyano, S. A weighted profile based method for protein-RNA interacting residue prediction. Transactions on Computational Systems Biology. Lecture Notes in Computer Science. 3939: 123-139, 2006.
  6. Li, C., Suzuki, S., Ge, Q.-W., Nakata, M., Matsuno, H., Miyano, S. Structural modeling and analysis of signaling pathways based on petri nets. J. Bioinf. Comput. Biol. 4(5):1119-1140, 2006. (On modeling and analyzing signaling pathways with inhibitory interactions based on Petri net. Extended abstract: Proc. The 2005 Internatinal Joint Conference of InCoB, AASBi and KSBI (BIOINFO2005), 348-353, 2005.).
  7. Matsuno, H., Inouye, S.-T., Okitsu, Y., Fujii, Y., Miyano, S. A new regulatory interactions suggested by simulations for circadian genetic control mechanism in mammals. J. Bioinf. Comput. Biol. 4(1): 139-154, 2006. (Extended abstract: Matsuno, H., Inouye, S-T., Okitsu, Y., Fujii, Y., Miyano, S. Proc. The 3rd Asia-Pacific Bioinformatics Conference 2005, Imperial College Press.)
  8. Matsuno, H., Li, C., Miyano, S. Petri net based description for systematic understanding of biological pathways. IEICE Trans. Fundamentals, E89-A (11): 3166-3174, 2006.
  9. Nagasaki, M.*, Yamaguchi, R.*, Yoshida, R., Imoto, S., Doi, A., Tamada, Y., Matsuno, H., Miyano, S., Higuchi, T. Genomic data assimilation for estimating hybrid functional Petri net from time-course gene expression data. Genome Informatics. 17(1): 46-61, 2006. (* These authors equally contributed)
  10. Nakamichi, R., Imoto, S., Miyano, S. Statistical model selection method to analyze combinatorial effects of SNPs and environmental factors for binary disease. International J. Artificial Intelligence Tools, 15(5), 711-724, 2006.
  11. Okada, R., Sugii, M., Matsuno, H., Miyano, S. Machine learning prediction of amino acid patterns in protein N-myristoylation. Lecture Notes in Bioinformatics. 4146: 4-14, 2006. (2006 Workshop on Pattern Recognition In Bioinformatics (PRIB'06))
  12. Saito, A., Nagasaki, M., Doi, A., Ueno, K., Miyano, S. Cell fate simulation model of gustatory neurons with microRNAs double-negative feedback loops by hybrid functional Petri net with extension. Genome Informatics. 17(1): 100-111, 2006.
  13. Takei, Y., Kawakoshi, A., Tsukada, T., Yuge, S., Ogoshi, M., Inoue, K., Hyodo, S., Bannai, H., Miyano, S. Contribution of comparative fish studies to general endocrinology: structure and function of some osmoregulatory hormones. J. Experimental Zoology. Part A, Comparative Experimental Biology. 305(9):787-798, 2006.
  14. Tamada, Y., Imoto, S., Miyano, S. Estimating gene networks from gene expression data utilizing biological information. Proc. Inst. Statist. Math. 54(2): 333-356, 2006.
  15. Tasaki, S., Nagasaki, M., Oyama, M., Hata, H., Ueno, K., Yoshida, R., Higuchi, T., Sugano, S., Miyano, S. Modeling and estimation of dynamic EGFR pathway by data assimilation approach using time series proteomic data. Genome Informatics. 17(2): 226-228, 2006.
  16. Termier, A., Tamada, Y., Imoto, S., Washio, T., Higuchi, T. From closed tree mining towards closed DAG mining. Proc. International Workshop on Data Mining and Statistical Science. 1-7, 2006. (Peer-reviewed conference paper)
  17. Washio, T. Higuchi, T., Imoto, S., Tamada, Y., Sato, K., Motoda, H. Graph mining and its application to statistical modeling (in Japanese with English abstract). Proc. Inst. Statist. Math. 54(2):315-332, 2006.
  18. Yoshida, R., Higuchi, T., Imoto, S., Miyano, S. ArrayCluster: an analytic tool for clustering, data visualization and module finder on gene expression profiles. Bioinformatics. 22(12):1538-1539, 2006.
  19. Yoshida, R., Numata, K., Imoto, S., Nagasaki, M., Doi, A., Ueno, K., Miyano, S. A statistical framework for genome-wide discovery of biomarker splice variations with GeneChip Human Exon 1.0 ST arrays. Genome Informatics. 17(1): 88-99, 2006.

2005

  1. De Hoon, Michiel J. L., Makita, Y., Nakai, K., Miyano, S. Prediction of transcriptional terminators in Bacillus subtilis and related species. PLoS Computational Biology. 1(3): e25, 2005.
  2. Hirose, O., Nariai, N., Tamada, Y., Bannai, H., Imoto, S., Miyano, S. Estimating gene networks from expression data and binding location data via boolean networks. Lecture Notes in Computer Science. 3482: 349-356, 2005. (Proc. 1st International Conference on Computational Science and Its Applications (Workshop on Data Mining and Bioinformatics)).
  3. Imoto, S., Matsuno, H., Miyano, S. Gene networks: estimation, modeling and simulation. in R. Eils and A. Kriete (Eds.), Computational Systems Biology, Academic Press, 205-228, 2005.
  4. Kato, M., Nagasaki, M., Doi, A., Miyano, S. Automatic drawing of biological networks using cross cost and subcomponent data. Genome Informatics. 16(2): 22-31, 2005.
  5. Kitakaze, H., Matsuno, H., Ikeda, N., Miyano, S. Prediction of debacle points for robustness of biological pathways by using recurrent neural networks. Genome Informatics. 16(1): 192-202, 2005.
  6. Makita, Y., De Hoon, M.J., Ogasawara, N., Miyano, S., Nakai, K. Bayesian joint prediction of associated transcription factors in Bacillus subtilis. Pacific Symposium on Biocomputing. 10: 507-518, 2005.
  7. Nagasaki, M., Doi, A., Matsuno, H., Miyano, S. Petri net modeling of biological pathways. Proc. Algebraic Biology 2005 (Universal Academy Press). 19-31, 2005.
  8. Nagasaki, M., Doi, A., Matsuno, H., Miyano, S. Computational modeling of biological processes with Petri net based architecture. In “Bioinformatics Technologies” (Y.P. Chen, ed). Springer Press. 179-243, 2005.
  9. Nariai, N., Tamada, Y., Imoto, S., Miyano, S. Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data. Bioinformatics. 21: ii206-ii212, 2005.
  10. Ohtsubo, S., Iida, A., Nitta, K., Tanaka, T., Yamada, R., Ohnishi, Y., Maeda, S., Tsunoda, T., Takei, T., Obara, W., Akiyama, F., Ito, K., Honda, K., Uchida, K., Tsuchiya, K., Yumura, W., Ujiie, T., Nagane, Y., Miyano, S., Suzuki, Y., Narita, I., Gejyo, F., Fujioka, T., Nihei, H., Nakamura, Y. Association of a single-nucleotide polymorphism in the immunoglobulin mu-binding protein 2 gene with immunoglobulin A nephropathy. J. Hum. Genet. 50(1): 30-35, 2005.
  11. Ott, S., Hansen, A., Kim, S.-Y., Miyano, S. Superiority of network motifs over optimal networks and an application to the revelation of gene network evolution. Bioinformatics. 21(2): 227-238, 2005.
  12. Tamada, Y., Bannai, H., Imoto, S., Katayama, T., Kanehisa, M., Miyano, S. Utilizing evolutionary information and gene expression data for estimating gene networks with Bayesian network models. J. Bioinformatics and Computational Biology. 3(6): 1295-1313, 2005.
  13. Tamada, Y., Imoto, S., Tashiro, K., Kuhara, S., Miyano, S. Identifying drug active pathways from gene networks estimated by gene expression data. Genome Informatics. 16(1): 182-191, 2005.
  14. Yoshida, R., Imoto, S., Higuchi, T. A penalized likelihood estimation on transcriptional module-based clustering. Proc. 1st International Workshop on Data Mining and Bioinformatics, Lecture Note in Comupter Science. 3482:389-401, 2005. (DMBio2005: Refereed conference).
  15. Yoshida, R., Imoto, S., Higuchi, T. Estimating time-dependent gene networks from time series microarray data by dynamic linear models with Markov switching. Proc. 4th Computational Systems Bioinformatics, 289-298, 2005. (CSB2005: Refereed conference).

2004

  1. Ando, T., Imoto, S., Konishi, S. Adaptive learning machines for nonlinear classification and Bayesian information criterion. Bulletin of Informatics and Cybernetics. 36: 147-162, 2004.
  2. Ando, T., Imoto, S., Miyano, S. Bayesian network and radial basis function network regression for nonlinear modeling of genetic network. Proc. Third International Conference on Information. 561-564, 2004.
  3. Ando, T., Imoto, S., Miyano, S. Functional data analysis of the dynamics of gene regulatory networks. Proc. Knowledge Exploration in Life Science Informatics KELSI2004. Lecture Notes in Artificial Intelligence. 3303: 69-83, 2004.
  4. Ando, T., Imoto, S., Miyano, S. Kernel mixture survival models for identifying cancer subtypes, predicting patient's cancer types and survival probabilities. Genome Informatics. 15(2):201-210, 2004.
  5. Araki, Y., Konishi, S., Imoto, S. Functional discriminant analysis for time-seriese gene expression data via radial basis function expansion. Proc. COMPSTAT 2004. 613-620, Physica-Verlag/Springer, 2004. (COMPSTAT2004: Refereed conference).
  6. Bannai, H., Hyyrö, H., Shinohara, A., Takeda, M., Nakai, K., Miyano, S. An O(N2) algorithm for discovering optimal Boolean pattern pairs. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 1(4): 159-170, 2004. (Extended abstract: Bannai, H., Hyyrö, H., Shinohara, A., Takeda, M., Nakai, K., Miyano, S. Finding optimal pairs of patterns. Proc. 4th International Workshop on Algorithms in Bioinformatics (WABI 2004). Lecture Notes in Bioinformatics. 3240:450-462, 2004.)
  7. Bannai, H., Inenaga, S., Shinohara, A., Takeda, M., Miyano, S. Efficiently finding regulatory elements using correlation with gene expression. J. Bioinformatics and Computational Biology. 2(2):273-288, 2004.
  8. De Hoon, M.J.L., Imoto, S., Kobayashi, K., Ogasawara, N., Miyano, S. Predicting the operon structure of Bacillus subtilis using operon length, intergene distance, and gene expression information. Pacific Symposium on Biocomputing. 9:276-287, 2004.
  9. De Hoon, M.J.L., Imoto, S., Nolan, J., Miyano, S. Open source clustering software. Bioinformatics. 20(9):1453-1454, 2004.
  10. De Hoon, M.J.L., Makita, Y., Imoto, S., Kobayashi, K., Ogasawara, N., Nakai, K., Miyano, S. Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data. Bioinformatics, 20(Suppl.1):i101-i108, 2004.
  11. Doi, A., Fujita, S., Matsuno, H., Nagasaki, M., Miyano, S. Constructing biological pathway models with hybrid functional Petri nets. In Silico Biology. 4(3):271-291, 2004.
  12. Fujita, S., Matsui, M., Matsuno, H., Miyano, S. Modeling and simulation of fission yeast cell cycle on hybrid functional Petri net. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. E87-A(11):2919-2928, 2004.
  13. Imoto, S., Higuchi, T., Kim, S., Jeong, E., Miyano, S. Residual bootstrapping and median filtering for robust estimation of gene networks from microarray data. Proc. 2nd Computational Methods in Systems Biology. Lecture Notes in Bioinformatics. 3082:149-160, 2004.
  14. Imoto, S., Higuchi, T., Goto, T., Tashiro, K., Kuhara, S., Miyano, S. Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. J. Bioinformatics and Computational Biology. 2(1):77-98, 2004.
  15. Inenaga, S., Bannai, H., Hyyrö, H., Shinohara, A., Takeda, M., Nakai, K., Miyano, S. Finding optimal pairs of cooperative and competing patterns with bounded distance. Proc. 7th International Conference on Discovery Science (DS 2004). Lecture Notes in Artificial Intelligence. 3245:32-46, 2004.
  16. Jeong, E., Chung, I., Miyano, S. A neural network method for identification of RNA-interacting residues in protein. Genome Informatics. 15(1):105-116, 2004.
  17. Kim, S., Imoto, S., Miyano, S. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Biosystems. 75(1-3): 57-65, 2004. (Extended abstract: Proceedings of First Computational Methods in Systems Biology. Lecture Notes in Computer Science. 2602:104-113, 2003.)
  18. Matsui, M., Fujita, S., Suzuki, S., Matsuno, H., Miyano, S. Simulated cell division processes of the Xenopus cell cycle pathway by Genomic Object Net. J. Integrative Bioinformatics. 3: 95-104, 2004. (http://journal.imbio.de/index.php?paper_id=3, 2004.)
  19. Miyano, S. Computational systems biology. Proc. Third International Conference on Information (Li, L. and Yen, K.K., Eds.). 9-14, 2004.
  20. Nagasaki, M., Doi, A., Matsuno, H., Miyano, S. Integrating biopathway databases for large-scale modeling and simulation. Proc. Second Asia-Pacific Bioinformatics Conference (APBC2004) (Y.P. Chen, Ed.). Conferences in Research and Practice in Information Technology. 29: 43-52, 2004.
  21. Nagasaki, M., Doi, A., Matsuno, H., Miyano, S. A versatile Petri net based architecture for modeling and simulation of complex biological processes. Genome Informatics. 15(1):180-197, 2004.
  22. Nakamichi, R., Imoto, S., Miyano, S. Case-control study of binary trait considering interactions between SNPs and environmental effects using logistic regression. Proc. 4th IEEE Bioinformatics and Bioengineering. IEEE Press. 73-78, 2004.
  23. Nakano, M., Noda, R., Kitakaze, H., Matsuno, H., Miyano, S. XML pathway file conversion between Genomic Object Net and SBML. Proc. The Third International Conference on Information. 585-588, 2004.
  24. Nariai, N., Kim, S., Imoto, S., Miyano, S. Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks. Pacific Symposium on Biocomputing. 9:336-347, 2004.
  25. Ott, S., Imoto, S., Miyano, S. Finding optimal models for small gene networks. Pacific Symposium on Biocomputing. 9:557-567, 2004.
  26. Takei, Y., Inoue, K., Ogoshi, M., Kawahara, T., Bannai, H., Miyano, S. Identification of novel adrenomedullin in mammals: a potent cardiovascular and renal regulator. FEBS Letters, 556:53-58, 2004.
  27. Yoshida, R., Higuchi, T., Imoto, S. A mixed factors model for dimension reduction and extraction of a group structure in gene expression data. Proc. 3rd Computational Systems Bioinformatics. 161-172, 2004. (CSB2004: Refereed conference).

2003

  1. Akutsu, T., Kuhara, S., Maruyama, O., Miyano, S. Identification of genetic networks by strategic gene disruptions and gene overexpressions under a boolean model. Theoretical Computer Science. 298(1):235-251, 2003. (Extended abstract: Proceedings of 9th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA'98), 695-702, 1998.)
  2. Akutsu, T., Miyano, S., Kuhara, S. A simple greedy algorithm for finding functional relations: efficient implementation and average case analysis. Theoretical Computer Science. 292(2):481-495, 2003. (Extended abstract: Proceedings of Third International Conference on Discovery Science. Lecture Notes in Artificial Intelligence. 1967:86-98, 2000.)
  3. Bannai, H., Inenaga, S., Shinohara, A., Takeda, M. Inferring strings from graphs and arrays. Proc. 28th International Symposium on Mathematical Foundations of Computer Science (MFCS2003), Lecture Notes in Computer Science. 2747: 208-217, 2003. (Peer-reviewed paper)
  4. De Hoon, M., Imoto, S., Kobayashi, K., Ogasawara, N., Miyano, S. Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations. Pacific Symposium on Biocomputing. 8:17-28, 2003.
  5. Doi, A., Nagasaki, M., Matsuno, H., Miyano, S. Genomic Object Net: II. Modelling biopathways by hybrid functional Petri net with extension. Applied Bioinformatics. 2(3):185-188, 2003.
  6. Imoto, S., Kim, S., Goto, T., Aburatani, S., Tashiro, K., Kuhara, S., Miyano, S. Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. Journal of Bioinformatics and Computational Biology. 1(2):231-252, 2003. (Extended abstract: Proceedings of IEEE Computer Society Bioinformatics Conference. 219-227, 2002.) (Extended abstract: Proceedings of Second Computational Systems Bioinformatics, 104-113, 2003.)
  7. Imoto, S., Konishi, S. Selection of smoothing parameters in B-spline nonparametric regression models using information criteria. Annals of the Institute of Statistical Mathematics. 55(4): 671-687, 2003.
  8. Imoto, S., Savoie, C.J., Aburatani, S., Kim, S., Tashiro, K., Kuhara, S., Miyano, S. Use of gene networks for identifying and validating drug targets. Journal of Bioinformatics and Computational Biology. 1(3):459-474, 2003.
  9. Kim, S., Imoto, S., Miyano, S. Inferring gene networks from time series microarray data using dynamic Bayesian networks. Briefings in Bioinformatics. 4(3):228-235, 2003.
  10. Konishi, S., Ando, T., Imoto, S. Bayesian information criteria and smoothing parameter selection in radial basis function networks. Biometrika. 91(1): 27-43, 2003.
  11. Matsuno, H., Murakami, R., Yamane, R., Yamasaki, N., Fujita, S., Yoshimori, H., Miyano, S. Boundary formation by notch signaling in Drosophila multicellular systems: experimental observations and gene network modeling by Genomic Object Net. Pacific Symposium on Biocomputing. 8:152-163, 2003.
  12. Matsuno, H., Tanaka, Y., Aoshima, H., Doi, A., Matsui, M., Miyano, S. Biopathways representation and simulation on hybrid functional Petri net. In Silico Biology. 3(3): 389-404, 2003.
  13. Matsuno, H., Fujita, S., Doi, A., Nagasaki, M., Miyano, S. Towards biopathway modeling and simulation. Proceedings of 24th International Conference on Applications and Theory of Petri Nets (ICATPN 2003). Lecture Notes in Computer Science. 2679:3-22, 2003.
  14. Miyano, S. Inference, modeling and simulation of gene networks. Proceedings of International Workshop on Computational Methods in Systems Biology. Lecture Notes in Computer Science. 2602:207-211, 2003.
  15. Nagasaki, M. Doi, A., Matsuno, H., Miyano, S. Genomic Object Net: I. a platform for modeling and simulating biopathways. Applied Bioinformatics. 2(3):181-184, 2003.
  16. Nagasaki, M., Doi, A., Matsuno, H., Miyano, S. Recreating biopathway databases towards simulation. Proceedings of International Workshop on Computational Methods in Systems Biology. Lecture Notes in Computer Science. 2602:191-192, 2003.
  17. Obara, W., Iida, A., Suzuki, Y., Tanaka, T., Akiyama, F., Maeda, S., Ohnishi, Y., Yamada, R., Tsunoda, T., Takei, T., Ito, K., Honda, K., Uchida, K., Tsuchiya, K., Yumura, W., Ujiie, T., Nagane, Y., Nitta, K., Miyano, S., Narita, I., Gejyo, F., Nihei, H., Fujioka, T., Nakamura, Y. Association of single-nucleotide polymorphisms in the polymeric immunoglobulin receptor gene with immunoglobulin A nephropathy (IgAN) in Japanese patients. J Hum Genet. 48(6):293-299, 2003.
  18. Ott, S., Tamada, Y., Bannai, H., Nakai, K., Miyano, S. Intrasplicing - analysis of long intron sequences. Pacific Symposium on Biocomputing. 8:339-350, 2003.
  19. Ott, S., Miyano, S. Finding optimal gene networks using biological constraints. Genome Informatics. 14:124-133, 2003.
  20. Savoie, C.J., Aburatani, S., Watanabe, S., Eguchi, Y., Muta, S., Imoto, S., Miyano, S., Kuhara, S., Tashiro, K. Use of gene networks from full genome microarray libraries to identify functionally relevant drug-affected genes and gene regulation cascades. DNA Research. 10(1):19-25, 2003.
  21. Sumii, E., Bannai, H. The extension of ML with hypothetical views for discovery science: formalization and implementation. Journal of Functional and Logic Programming, Vol. 2003: Special Issue 1, 2003.
  22. Takeda, M., Inenaga, S., Bannai, H., Shinohara, A., Arikawa, S. Discovering most classificatory patterns for very expressive pattern classes. Proc. 6th International Conference on Discovery Science (DS 2003), Lecture Notes in Computer Science. 2843: 486-493, 2003. (Peer-reviewed paper)
  23. Takei, Y., Inoue, K., Ogoshi, M., Kawahara, T., Bannai, H., Miyano, S. Identification of novel adrenomedullin in mammals: a potent cardiovascular and renal regulator. FEBS Letters, 556(1-3):53-58, 2003.
  24. Tamada, Y., Kim, S., Bannai, H., Imoto, S., Tashiro, K., Kuhara, S., Miyano, S. Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection. Bioinformatics. 19(Suppl.2):ii227-ii236, 2003.

2002

  1. Akiyama, F., Tanaka, T., Yamada, R., Ohnishi, Y., Tsunoda, T., Maeda, S., Takei, T., Obara, W., Ito, K., Honda, K., Uchida, K., Tsuchiya, K., Nitta, K., Yumura, W., Nihei, H., Ujiie, T., Nagane, Y., Miyano, S., Suzuki, Y., Fujioka, T., Narita, I., Gejyo, F., Nakamura, Y. Single-nucleotide polymorphisms in the class II region of the major histocompatibility complex in Japanese patients with immunoglobulin A nephropathy. J. Hum. Genet. 47(10):532-538, 2002.
  2. Akutsu, T., Miyano, S. Selecting informative genes for cancer classification using gene expression data. Computational and Statistical Approaches to Genomics (W. Zhang and I. Shmulevich eds.). Kluwer Academic Pub. 79-92, 2002.
  3. Bannai, H. Inenaga, S., Shinohara, A., Takeda, M., Miyano, S. A string pattern regression algorithm and its application to pattern discovery in long introns. Genome Informatics. 13:3-11, 2002.
  4. Bannai, H., Tamada, Y., Maruyama, O., Nakai, K., Miyano, S. Extensive feature detection of N-terminal protein sorting signals. Bioinformatics. 18(2):298-305, 2002.
  5. De Hoon, M., Imoto, S., Miyano, S. Inferring gene regulatory networks from time-ordered gene expression data using differential equations. Proceedings of Fifth International Conference on Discovery Science. Lecture Notes in Artificial Intelligence. 2534:267-274, 2002.
  6. De Hoon, M.J.L., Imoto, S., Miyano, S. Statistical analysis of a small set of time-ordered gene expression data using linear splines. Bioinformatics. 18(11):1477-1485, 2002.
  7. Imoto, S., Goto, T., Miyano, S. Estimation of genetic networks and functional structures between genes by using Bayesian network and nonparametric regression. Pacific Symposium on Biocomputing. 7:175-186, 2002.
  8. Inenaga, S., Bannai, H., Shinohara, A., Takeda, M., Arikawa, S. Discovering best variable-length-don't-care patterns. Proc. 5th International Conference on Discovery Science (DS2002), Lecture Notes in Computer Science. 2534: 86-97, 2002. (Peer-reviewed paper)
  9. Inenaga, S., Shinohara, A., Takeda, M., Bannai, H., Arikawa, S. Space-economical construction of index structures for all-suffixes of a string. Proc. 27th. International Symposium on Mathematical Foundation of Computer Science (MFCS2002), Lecture Notes in Computer Science. 2420: 341-352, 2002. (Peer-reviewed paper)
  10. Maruyama, O., Bannai, H., Tamada, Y., Kuhara, S., Miyano, S. Fast algorithm for extracting multiple unordered short motifs using bit operations. Information Sciences. 146(1-4):115-126, 2002.
  11. Maruyama, O., Shoudai, T., Miyano, S. Toward drawing an atlas of hypothesis classes: approximating a hypothesis via another hypothesis model. Proc. 5th International Conference on Discovery Science (DS2002). Lecture Notes in Computer Science. 2534:220-232, 2002.
  12. Sumii, E., Bannai, H. VMlambda: a functional calculus for scientific discovery. Proc. 6th International Symposium on Functional and Logic Programming (FLOPS 2002), Lecture Notes in Computer Science. 2441: 290-304, 2002. (Peer-reviewed paper)
  13. Takei, T., Iida, A., Nitta, K., Tanaka, T., Ohnishi, Y., Yamada, R., Maeda, S., Tsunoda, T., Takeoka, S., Ito, K., Honda, K., Uchida, K., Tsuchiya, K., Suzuki, Y., Fujioka, T., Ujiie, T., Nagane, Y., Miyano, S., Narita, I., Gejyo, F., Nihei, H., Nakamura, Y. Association between single-nucleotide polymorphisms in selectin genes and immunoglobulin A nephropathy. Am. J. Hum. Genet. 70(3):781-786, 2002.
  14. Tamada, Y., Bannai, H., Maruyama, O., Miyano, S. Foundations of designing computational knowledge discovery processes. Progress in Discovery Science. Lecture Notes in Computer Science. 2281:459-470, 2002.

2001

  1. Bannai, H., Tamada, Y., Maruyama, O., Miyano, S. VML: a view modeling language for computational knowledge discovery. Proceedings of Fourth International Conference on Discovery Science. Lecture Notes in Artificial Intelligence. 2226:30-44, 2001.
  2. Bannai, H., Tamada, Y., Maruyama, O., Miyano, S. HypothesisCreator: Concepts for accelerating the computational knowledge discovery process. Electronic Transactions on Artificial Intelligence. 5:73-83, 2001. (Electronic version: Linkoping Electronic Articles in Computer and Information Science. Issue: Vol. 6 (2001), No. 019. URL: http://www.ep.liu.se/ea/cis/2001/019/.)
  3. Bannai, H., Tamada, Y., Maruyama, O., Nakai, K., Miyano, S. Views: fundamental building blocks in the process of knowledge discovery. Proceedings of the 14th International FLAIRS Conference. AAAI Press. 233-238, 2001.
  4. Maruyama, O., Shoudai, T., Furuichi, E., Kuhara, S., Miyano, S. Learning conformation rules. Proceedings of Fourth International Conference on Discovery Science. Lecture Notes in Artificial Intelligence. 2226:243-257, 2001.
  5. Matsuno, H., Doi, A., Hirata, Y., Miyano, S. XML Documentation of biopathways and their simulations in Genomic Object Net. Genome Informatics. 12:54-62, 2001.
  6. Nakayashiki, T., Ebihara, K., Bannai, H., Nakamura, Y. Yeast [PSI+] “prions” that are crosstransmissible and susceptible beyond a species barrier through a quasi-prion state. Molecular Cell, Vol. 7(6): 1121-1130, 2001.
  7. Onami, S., Hamahashi, S., Nagasaki, M., Miyano, S., Kitano, H. Automatic acquisition of cell lineage through 4D micorscopy and analysis of early C. elegans embryogenesis. Foundations of Systems Biology, Kitano, H. (ed.), MIT Press. 39-55, 2001.
  8. Sim, K.L., Uchida, T., Miyano, S. ProDDO: a database of disordered proteins form the Protein Data Bank (PDB). Bioinformatics 17(4):379-380, 2001.

2000

  1. Akutsu, T., Miyano, S., Kuhara, S. Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics. 16(8):727-734, 2000.
  2. Akutsu, T., Miyano, S., Kuhara, S. Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function. Journal of Computational Biology. 7(3-4):331-343, 2000. (Extended abstract: Proceedings of Fourth Annual International Conference on Computational Molecular Biology. 8-24, 2000.)
  3. Akutsu, T., Miyano, S., Kuhara, S. Algorithms for inferring qualitative models of biological networks. Pacific Symposium on Biocomputing. 5:293-304, 2000.
  4. Maruyama, O., Miyano, S. Design sspects of discovery systems. IEICE Transactions on Information and Systems. E83-D(1):61-70, 2000.
  5. Matsuno, H., Doi, A., Nagasaki, M., Miyano, S. Hybrid Petri net representation of gene regulatory network. Pacific Symposium on Biocomputing. 5:341-352, 2000.
  6. Miyano, S., Shinohara, A., Shinohara, T. Polynomial-time learning of elementary formal systems. New Generation Computing. 18(3):217-242, 2000.
  7. Tanaka, M., Nakazono, S., Matsuno, H., Tsujimoto, H., Kitamura, Y., Miyano, S. Intelligent system for topic survery in MEDLINE by keyword recommendation and learning text characteristics. Genome Informatics. 11:73-82, 2000.

1999

  1. Akutsu, T., Miyano, S. On the approximation of protein threading. Theoretical Computer Science. 210:261-275, 1999. (Extended abstract: Proceedings of the First Annual International Conference on Research in Computational Molecular Biology. 3-8, 1997.)
  2. Akutsu, T., Miyano, S., Kuhara, S. Identification of genetic networks from a small number of gene expression patterns under the boolean network model. Pacific Symposium on Biocomputing. 4:17-28, 1999.
  3. Bannai, H., Miyano, S. A definition of discovery in terms of generalized descriptional complexity. Proceedings of Second International Conference on Discovery Science. Lecture Notes in Artificial Intelligence. 1721:316-318, 1999.
  4. Maruyama, O., Uchida, T., Sim, K.L., Miyano, S. Designing views in HypothesisCreator: System for assisting in discovery. Proceedings of Second International Conference on Discovery Science. Lecture Notes in Artificial Intelligence. 1721:115-127, 1999.
  5. Moriyama, T., Shinohara, A., Takeda, M., Maruyama, O., Goto, T., Miyano, S., Kuhara, S. A system to find genetic networks using weighted network model. Genome Informatics. 10:186-195, 1999.
  6. Nagasaki, M., Onami, S., Miyano, S., Kitano, H. Bio-calculus: its concept and molecular interaction. Genome Informatics. 10:133-143, 1999.
  7. Yasuda, T., Bannai, H., Onami, S., Miyano, S., Kitano, H. Towards automatic construction of cell-linearge of C. elegans from Normarski DIC microscope images. Genome Informatics. 10:144-154, 1999.

1998

  1. Akutsu, T., Maruyama, O., Miyano, S., Kuhara, S. A system for identifying genetic networks from gene expression patterns produced by gene disruptions and overexpressions. Genome Informatics. 9:151-160, 1998.
  2. Maruyama, O., Uchida, T., Shoudai, T., Miyano, S. Toward Genomic Hypothesis Creator: View Designer for Discovery. Proceedings of First International Conference on Discovery Science. Lecture Notes in Artificial Intelligence. 1532:105-116, 1998.
  3. Noda, K., Shinohara, A., Takeda, M., Matsumoto, S., Miyano, S., Kuhara, S. Finding genetic network from experiments by weighted network model. Genome Informatics. 9:141-150, 1998.
  4. Usuzaka, S., Sim, K.L., Tanaka, M., Matsuno, H., Miyano, S. A machine learning approach to reducing the work of experts in article selection from database: a case study for regulatory relations of S. cerevisiae genes in MEDLINE. Genome Informatics. 9:91-101, 1998.

1997 - 1978

  1. Miyano, S. Genome Informatics: New Frontiers of Computer Science and Biosciences. Cooperative Databases and Applications (Advances Database Research and Development Series Vol. 7). edited by Kambayashi, Y. and Yokota, K. (World Scientific), 12-21, 1997.
  2. Yamaguchi, A., Nakano, K., Miyano, S. An approximation algorithm for the minimum common supertree problem. Nordic J. Computing. 4(2):303-316, 1997.
  3. Furukawa, N., Matsumoto, S., Shinohara, A., Shoudai, T., Miyano, S. HAKKE: A multi-strategy prediction system for sequences. Genome Informatics. 7:98-107, 1996.
  4. Maruyama, O., Miyano, S. Taking a walk on a graph. Mathematica Japonica. 43(3):595-606, 1996.
  5. Maruyama, O., Miyano, S. Inferring a tree from walks. Theoretical Computer Science. 161(1-2):289-300, 1996.
  6. Tateishi, E., Miyano, S. A greedy strategy for finding motifs from yes-no examples. Pacific Symposium on Biocomputing. 1:599-613, 1996.
  7. Tateishi, E., Maruyama, O., Miyano, S. Extracting best consensus motifs from positive and negative examples. Proceedings of the 13th Annual Symposium on Theoretical Aspects of Computer Science. Lecture Notes in Computer Science. 1046:219-230, 1996.
  8. Miyano, S. Learning theory towards genome informatics. IEICE Transactions on Information and Systems. E78-D(5):560-567, 1995. (Invited paper: Miyano, S. Learning theory towards genome informatics. Proc. 4th International Workshop on Algorithmic Learning Theory, Lecture Notes in Computer Science. 744: 19-36, 1993.)
  9. Miyano, S., Shimozono, S., Maruyama, O. Some algorithmic problems arising from genome informatics. Advances in Computing Techniques - Algorithms, Databases and Prallel Processing. World Scientific, 45-59, 1995.
  10. Shimozono, S., Miyano, S. Complexity of finding alphabet indexing. IEICE Transactions on Information and Systems. E78-D(1):13-18, 1995.
  11. Shoudai, T., Miyano, S. Using maximal independent sets to solve problems in parallel. Theoretical Computer Science. 148(1):57-65, 1995.
  12. Uchida, T., Shoudai, T., Miyano, S. Parallel algorithms for refutation tree problem on elementary formal graph systems. IEICE Transactions on Information and Systems. E78-D(2):99-112, 1995.
  13. Shoudai, T., Lappe, M., Miyano, S., Shinohara, A., Okazaki, T., Arikawa, S., Uchida, T., Shimozono, S., Shinohara, T., Kuhara, S. BONSAI Garden: parallel knowledge discovery system for amino acid sequences. Proceedings of the Third International Conference on Intelligent Systems for Molecular Biology. AAAI Press. 359-366, 1995.
  14. Uchida, T., Shoudai, T., Miyano, S. Polynomial time algorithm solving the refutation tree problem for formal graph systems. Bulletin of Informatics and Cybernetics. 26(1-2):55-74, 1994.
  15. Arikawa, S., Kuhara, S., Miyano, S., Mukouchi, Y., Shinohara, A., Shinohara, T. A machine discovery from amino acid sequences by decision trees over regular patterns. New Generation Computing. 11(3):361-375, 1993.
  16. Arikawa, S., Shinohara, T., Miyano, S., Shinohara, A. More about learning elementary formal systems. Proceedings of Second International Workshop on Nonmonotonic and Inductive Logic. Lecture Notes in Computer Science. 659:107-117, 1993.
  17. Furuya, S., Miyano, S. NP-hard aspects in analogical reasoning. Bulletin of Informatics and Cybernetics. 25(3-4):155-159, 1993.
  18. Shimozono, S., Shinohara, A., Shinohara, T., Miyano, S., Kuhara, S., Arikawa, S. Finding alphabet indexing for decision trees over regular patterns: an approach to bioinformatical knowledge acquisition. Proceedings of the Twenty-Sixth Hawaii International Conference on System Sciences, Vol. I. IEEE Computer Society Press. 763-772, 1993.
  19. Shimozono, S., Shinohara, A., Shinohara, T., Miyano, S., Kuhara, S., Arikawa, S. Knowledge acquisition from amino acid sequences by machine learning system BONSAI. Transactions on Information Processing Society of Japan. 35(10):2009-2018, 1993.
  20. Shinohara, A., Shimozono, S., Uchida, T., Miyano, S. Kuhara, S., Arikawa, S. Running learning systems in parallel for machine discovery from sequences. Genome Informatics. 4:74-83, 1993.
  21. Shoudai, T., Miyano, S. A parallel algorithm for the maximal co-hitting set problem. IEICE Transactions on Information and Systems. E76-D(2):296-298, 1993.
  22. Uchida, T., Miyano, S. O(log*n) time parallel algorithm for computing bounded degree maximal subgraphs. Journal of Information Processing. 16(1):16-20, 1993.
  23. Arikawa, S., Kuhara, S., Miyano, S., Shinohara, A., Shinohara, T. A learning algorithm for elementary formal systems and its experiments on identification of transmembrane domains. Proceedings of the Twenty-Fifth Hawaii International Conference on System Science, Vol. I. IEEE Computer Society Press. 675-684, 1992.
  24. Arikawa, S., Miyano, S., Shinohara, A., Shimozono, S., Shinohara, T., Kuhara, S. Knowledge acquisition from amino acid sequences by learning algorithms. Proceedings of the Second Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop. 109-128, 1992.
  25. Arikawa, S., Miyano, S., Shinohara, A., Shinohara, T., Yamamoto, A. Algorithmic learning theory with elementary formal systems. IEICE Transactions on Information and Systems. E75-D(4):405-414, 1992.
  26. Furuya, S., Miyano, S. NP-complete problems on label updating calculation in ATMS. Bulletin of Informatics and Cybernetics. 25(1-2):1-5, 1992.
  27. Furuya, S., Miyano, S. Analogy is NP-hard. Proceedings of the Second Workshop on Algorithmic Learning Theory. 207-212, 1991.
  28. Miyano, S. Δp2complete lexicographcially first maximal subgraph problems. Theoretical Computer Science. 88(1):33-57, 1991.
  29. Miyano, S., Shinohara, A., Shinohara, T. Which classes of elementary formal systems are polynomial-time learnable? Proceedings of the Second Workshop on Algorithmic Learning Theory. 139-150, 1991.
  30. Shinohara, A., Miyano, S. Teachability in computational learning. New Generation Computing. 8(4):337-347, 1991.
  31. Miyano, S. Systematized approaches to the complexity of subgraph problems. Journal of Information Processing. 13(4):442-448, 1990.
  32. Arikawa, S., Haraguchi, M., Inoue, H., Kawasaki, Y., Miyahara, T., Miyano, S., Oshima, K., Sakai, H., Shinohara, T., Shiraishi, S., Takeda, M., Takeya, S., Yamamoto, A., Yuasa, H. The text database management system SIGMA: an improvement of the main engine. Proceedings of Berliner Informatik-Tage. 72-81, 1989.
  33. Miyano, S. The lexicographically first maximal subgraph problems - P-completeness and NC algorithms. Mathematical Systems Theory. 22(1):47-73, 1989.
  34. Miyano, S. A parallelizable lexicographically first edge-induced subgraph problem. Information Processing Letters. 27(2):75-78, 1988.
  35. Miyano, S. Indexing alternating finite automata and binary tree like circuits. Bulletin of Informatics and Cybernetics. 23(1-2):79-88, 1988.
  36. Miyano, S. Parallel complexity and P-complete problems. Proceedings of International Conference on Fifth Generation Computer Systems 1988. 532-541, 1988.
  37. Hayashi, T., Miyano, S. Finite tree automata on infinite trees. Bulletin of Informatics and Cybernetics. 21(3-4):71-82, 1985.
  38. Miyano, S. Remarks on two-way automata with weak-counters. Information Processing Letters. 18(2):105-107, 1984.
  39. Miyano, S., Hayashi, T. Alternating finite automata on ω-words. Theoretical Computer Science. 32(3):321-330, 1984.
  40. Miyano, S. Remarks on multihead pushdown automata and multihead stack automata. Journal of Computer and System Sciences. 27(1):116-124, 1983.
  41. Miyano, S. Two-way deterministic multi-weak-counter machines. Theoretical Computer Science. 21(1):27-32, 1982.
  42. Miyano, S. A hierarchy theorem for multihead stack-counter automata. Acta Informatica. 17(1):63-67, 1982.
  43. Miyano, S., Haraguchi, M. Recovery of incomplete tables under functional dependencies. Bulletin of Informatics and Cybernetics. 20(1-2):25-41, 1982.
  44. Miyano, S. One-way weak-stack-counter automata. Journal of Computer and System Sciences. 20(1):59-76, 1980.
  45. Miyano, S. On a lower bound of Shepherdson function. Memoirs of the Faculty of Science, Kyushu University, Ser. A. 33(2):257-267, 1979.
  46. Hirokawa, S., Miyano, S. A note on the regularity of fuzzy languages. Memoirs of the Faculty of Science, Kyushu University, Ser. A. 32(1):61-66, 1978.
  47. Miyano, S. On an automaton which recognizes a family of automata. Memoirs of the Faculty of Science, Kyushu University, Ser. A. 32(1):37-51, 1978.

Book Chapter

  1. Imoto, S., Tamada, Y., Araki, H., Miyano, S. Computational Drug Target Pathway Discovery: A Bayesian Network Approach. in H. Lu, B. Schokop, H. Zhao (Eds.), Handbook of Computational Statistics: Statistical Bioinformatics, Springer. In press.
  2. Fujita, A., Sato, J.R., Demasi, M.A.A., Miyano, S., Sogayar, M.C., Ferreira, C.E. An introduction to time-varying connectivity estimation for gene regulatory networks. Frank Emmert-Streib; Matthias Dehmer. (Org.). Medical Biostatistics for complex diseases. Weinheim, Germany: Wiley VCH Verlag, p.205-230, 2010.
  3. Mitou, N., Matsuno, H., Miyano, S., Inouye, S. Essential role of Ror gene in the interaction of feedback loops in mammalian circadian clocks. In press.
  4. Saito, A., Nagasaki, M., Miyano, S. Hybrid functional Petri net with extension for dynamic pathway modeling. In press.
  5. Imoto, S., Tamada, Y., Savoie, C.J., Miyano, S., Analysis of gene networks for drug target discovery and validation. Methods in Molecular Biology. 360:33-56, 2007. (Target Discovery and Validation, Volume 1, 33-56, 2007 (a volume of “Methods in Molecular Biology” series), Walker, J. and Sioud, M. (Eds.), Humana Press, USA.).
  6. Imoto, S., Miyano, S. Bayesian network approach to estimate gene networks. A. Mittal, A. Kassim and T. Tan (Eds.), Bayesian Network Technologies: Applications and Graphical Models, Idea Group Publishers, USA. 269-299, 2007. (Refereed book chapter).
  7. Nagasaki, M., Doi, A., Matsuno, H., Miyano, S. Computational modeling of biological processes with Petri net based architecture. In “Bioinformatics Technologies” (Y.P. Chen, ed). Springer Press. 179-243, 2005.
  8. Akutsu, T., Miyano, S. Selecting informative genes for cancer classification using gene expression data. Computational and Statistical Approaches to Genomics (W. Zhang and I. Shmulevich, Eds.). Kluwer Academic Pub. 79-92, 2002.
  9. Akutsu, T., Miyano, S. Selecting informative genes for cancer classification using gene expression data. Computational and Statistical Approaches to Genomics (W. Zhang and I. Shmulevich eds.). Kluwer Academic Pub. 79-92, 2002.
  10. Miyano, S. Genome Informatics: New Frontiers of Computer Science and Biosciences. Cooperative Databases and Applications (Advances Database Research and Development Series Vol. 7). edited by Kambayashi, Y. and Yokota, K. (World Scientific), 12-21, 1997.

Books

  1. Nagasaki, M., Saito, A., Doi, A., Matsuno, H., Miyano, S. “Foundations of Systems Biology - Using Cell Illustrator and Pathway Databases”. Springer, 2009. Springer
  2. Kurata, H., Miyano, S. （Japanese Translation). Uri Alon. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC, 2007）. Kyoritsu Shuppan Co. Ltd., 2008.
  3. Doi, A., Nagasaki, M., Saito, A., Matsuno, H., Miyano, S. “Yes, We Can Understand Systems Biolog! - Let's use Cell Illustrator” (in Japanese). Kyoritsu Shuppan, Co. Ltd., 2007.
  4. Miyano, S. “Parallel Algorithms - Theory and Design” (in Japanese). Kindai kagaku sha Co. Ltd., 1993.
  5. Arikawa, S., Miyano, S. “Automata and Computability” (in Japanese). Baifukan Co. Ltd., 1986.

Editions

  1. Ferreira, C.E., Miyano, S., Stadler, P.F. (Eds.) Advances in Bioinformatics and Computational Biology, Springer, 2010.
  2. DeLisi, C., Kanehisa, M., Miyano, S., Mohr, S., Wallach, I. (Eds.) Genome Informatics. 22. Imperial College Press, London, 2009.
  3. Brazma, A., Miyano, S., Akutsu, T. Proceedings of the 6th Asia-Pacific Bioinformatics Conference (APBC 2008).Imperial College Press, 2008. Link
  4. Knapp, E.-W., Benson, G., Holzhütter, H.-G., Kanehisa, M., Miyano, S. (Eds.) Genome Informatics. 20, 2008. Link
  5. Miyano, S., DeLisi, C., Holzhütter, H.-G., Kanehisa, M. (Eds.) Genome Informatics. 18, 2007. Link
  6. Sakakibara, Y., Smith, T.F., Kanehisa, M., Miyano, S., Takagi, T. (Eds.) Genome Informatics. 17(2), 2006. Link
  7. DeLisi, C., Kanehisa, M., Heinrich, R., Miyano, S. (Eds.) Genome Informatics. 17(1), 2006. Link
  8. Miyano, S. (Ed.) Special RECOMB 2005 Issue. J. Comp. Biol. 13(2), 2006.
  9. Heinrich, R., Mamitsuka, H., Kanehisa, M., Miyano, S., Takagi, T. (Eds.). Genome Informatics 16(2), 2005. Lionk
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