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写真説明
並列アルゴリズム　-理論と設計
システム生物学がわかる！ -セルイラストレーターを使ってみよう

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 (Li, L. and Yen, K.K., Eds.). 561-564, 2004.
  3. Ando, T., Imoto, S., Miyano, S. Functional data analysis of the dynamics of gene regulatory networks. 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-series gene expression data via radial basis function expansion. Proc. COMPSTAT 2004, Physica-Verlag/Springer, 613-620, 2004.
  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.
  7. Bannai, H., Hyyrö, H., Shinohara, A., Takeda, M., Nakai, K., Miyano, S. Finding optimal pairs of patterns. Lecture Notes in Bioinformatics. 3240:450-462, 2004.
  8. 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.
  9. 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.
  10. De Hoon, M.J.L., Imoto, S., Nolan, J., Miyano, S. Open source clustering software. Bioinformatics. 20(9):1453-1454, 2004.
  11. 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.
  12. 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.
  13. 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.
  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. Imoto, S., Higuchi, T., Kim, S., Jeong, E., Miyano, S. Residual bootstrapping and median filtering for robust estimation of gene networks from microarray data. Lecture Notes in Bioinformatics. 3082:149-160, 2004.
  16. 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. Lecture Notes in Artificial Intelligence. 3245:32-46, 2004.
  17. 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.
  18. 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.
  19. Konishi, S., Ando, T., Imoto, S. Bayesian information criteria and smoothing parameter selection in radial basis function networks. Biometrika. 91: 27-43, 2004.
  20. 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.)
  21. Miyano, S. Computational systems biology. Proc. Third International Conference on Information (Li, L. and Yen, K.K., Eds.). 9-14, 2004.
  22. 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.
  23. 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.
  24. 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.
  25. 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 (Li, L. and Yen, K.K., Eds.). 585-588, 2004.
  26. 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.
  27. Ott, S., Imoto, S., Miyano, S. Finding optimal models for small gene networks. Pacific Symposium on Biocomputing. 9:557-567, 2004.
  28. 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.
  29. 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. IEEE Press. 161-172, 2004.

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.
  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.
  3. Bannai, H., Inenaga, S., Shinohara, A., Takeda, M., Inferring strings from graphs and arrays. Lecture Notes in Computer Science. 2747: 208-217, 2003.
  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. J. Bioinformatics and Computational Biology. 1(2):231-252, 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, 55:671-687, 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. J. 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. 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.
  11. 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.
  12. Matsuno, H., Fujita, S., Doi, A., Nagasaki, M., Miyano, S. Towards biopathway modeling and simulation. Lecture Notes in Computer Science. 2679:3-22, 2003.
  13. Miyano, S. Inference, modeling and simulation of gene networks. Lecture Notes in Computer Science. 2602:207-211, 2003.
  14. 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.
  15. Nagasaki, M., Doi, A., Matsuno, H., Miyano, S. Recreating biopathway databases towards simulation. Lecture Notes in Computer Science. 2602:191-192, 2003.
  16. 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.
  17. Ott, S., Tamada, Y., Bannai, H., Nakai, K., Miyano, S. Intrasplicing - analysis of long intron sequences. Pacific Symposium on Biocomputing. 8:339-350, 2003.
  18. Ott, S., Miyano, S. Finding optimal gene networks using biological constraints. Genome Informatics. 14:124-133, 2003.
  19. 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.
  20. Sumii, E., Bannai, H., The extension of ML with hypothetical views for discovery science: formalization and implementation. J. Functional and Logic Programming, Special Issue 1, S03-01, 2003.
  21. Takeda, M., Inenaga, S., Bannai, H., Shinohara, A., Arikawa, S. Discovering most classificatory patterns for very expressive pattern classes. Lecture Notes in Computer Science. 2843: 486-493, 2003.
  22. 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.
  23. 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., Ott, S. Inferring a union of halfspaces from examples. Lecture Notes in Computer Science, 2387:117-126, 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. 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. Lecture Notes in Computer Science, 2534:86-97, 2002.
  9. Inenaga, S., Shinohara, A., Takeda, M., Bannai, H., Arikawa, S. Space-economical construction of index structures for all suffixes of a string. Lecture Notes in Computer Science, 2420:341-352, 2002.
  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. Lecture Notes in Computer Science. 2534:220-232, 2002.
  12. Sumii, E., Bannai, H. VM lambda: a functional calculusfor scientific discovery. Lecture Notes in Computer Science, 2441:290-304, 2002.
  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. Lecture Notes in Computer Science. 2281:459-470, 2002.

Book Chapters

  1. 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)
  2. Imoto, S., Matsuno, H., Miyano, S. Gene networks: estimation, modeling and simulation. R. Eils and A. Kriete (Eds.), Computational Systems Biology, Academic Press, 205-228, 2005. (Refereed book chapter).
  3. Nagasaki, M., Doi, A., Matsuno, H., Miyano, S. Computational modeling of biological processes with Petri net based architecture. Bioinformatics Technologies (Y.P. Chen, (Ed.)), Springer Press, 179-243, 2005. (Refereed book chapter)
  4. 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. (Refereed book chapter).

Editions

  1. Miyano, S., DeLisi, C., Holzhütter, H.-G., Kanehisa, M. (Eds.). Genome Informatics. 18, 2007.
  2. DeLisi, C., Kanehisa, M., Heinrich, R., Miyano, S. (Eds.). Genome Informatics. 17(1), 2006.
  3. Miyano, S. (Ed.). Special RECOMB 2005 Issue. J. Comp. Biol. 13(2), 2006.
  4. Sakakibara, Y., Smith, T.F., Kanehisa, M., Miyano, S., Takagi, T. (Eds.). Genome Informatics. 17(2), 2006.
  5. 宮野 悟，江口 至洋，金久 實，高木 利久，中井 謙太（編）．バイオインフォマティクス事典．共立出版．2006.
  6. Heinrich, T., DeLisi, C., Kanehisa, M., Miyano, S. (Eds.). Genome Informatics. 16 (1), 2005.
  7. Heinrich, R., Mamitsuka, H., Kanehisa, M., Miyano, S., Takagi, T. (Eds.). Genome Informatics. 16 (2), 2005.
  8. Miyano, S., Mesirov, J.P., Kasif, S., Istrail, S., Pevzner, P.A., Waterman, M.S. (Eds.). Proc. 9th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2005), Lecture Notes in Bioinformatics (Springer). Vol. 3500, 2005.
  9. Akutsu, T., Brusic, V., Miyano, S., Takagi, T., Kanehisa, M. (Eds.). Genome Informatics. 15(2), 2004.
  10. Mamitsuka, H., Smith, T.F., Holzhütter, H-G., Kanehisa, M., DeLisi, C., Heinrich, R., Miyano, S. (Eds.). Genome Informatics. 15(1), 2004.
  11. Gribskov, M., Kanehisa, M., Miyano, S., Takagi, T. (Eds.). Genome Informatics. 14, 2003.
  12. Lathrop, R., Nakai, K., Miyano, S., Takagi, T., Kanehisa, M. (Eds.). Genome Informatics. 13, 2002.

総説等

  1. 土井淳, 長崎正朗, 斉藤あゆむ, 宮野悟. 「システム生物学がわかる」． 共立出版．2007.
  2. 長崎正朗，土井淳，宮野悟．ダイナミックパスウェイモデリング言語 Cell System Markup Language (CSML). タンパク質 核酸 酵素．50(16 Suppl.):2269-2274, 2005.
  3. 藤井靖，松野浩嗣，宮野悟，井上愼一．ハイブリッド関数ペトリネットによる哺乳類の時計遺伝子機構のモデル化とシミュレーション．時間生物学．11(1):8-16, 2005.
  4. 土井淳，長崎正朗，松野浩嗣，宮野悟．Genomic Object Netによるパスウエイの表現とシミュレーション．ゲノミクス・プロテオミクスの新展開～生物情報の解析と応用～（今中忠之編）．エヌ・ティー・エス． 930-937，2004.
  5. 土井淳，長崎正朗，松野浩嗣，宮野悟．生命パスウエイのモデル化・可視化技術と創薬研究への応用．月刊薬事．46(7):1265-1272, 2004.
  6. 宮野　悟．ゲノムからバイオインフォマティクスへ．現代医療．36(5): 1018-1021, 2004.
  7. 宮野 悟，松野浩嗣，倉田 博之．システム生物学．ゲノム研究実験ハンドブック（辻本豪三，田中利男編集），49-54, 羊土社，2004.
  8. 宮野　悟．バイオパスウェイシミュレーション－医学とコンピュータ．Molecular Medicine. 41:328-331, 2004.
  9. 土井淳，長崎正朗，松野浩嗣，宮野悟．細胞反応のシミュレーション．わかる実験医学シリーズ「バイオインフォマティクスがわかる」．菅原秀明編集．羊土社．pp.81-84, 2003.
  10. 長崎正朗, 土井淳, 松野浩嗣，宮野悟．バイオパスウェイモデリングとシミュレーションを実現するためのシステム －Genomic Object Net－． 人工知能学会誌 18(1):8-13, 2003.
  11. 松野浩嗣, 宮野悟. バイオシミュレーションツールGenomic Object Net～生命システムをわかりやすくモデル化・視覚化できる～. 実験医学, 20(13):1873-1878, 2002.
  12. 松野浩嗣，宮野悟．パスウェイをデジタル化する．蛋白質　核酸　酵素，47(15):2062-2070, 2002.
  13. 宮野悟，Christopher J. Savoie．バイオインフォマティクスの創薬応用．実験医学，20(18):2632-2637,2002.
  14. 宮野悟．「ゲノム情報学とシステムバイオロジー」．分子生物学イラストレイティッド（田村 隆明・山本 雅（編））．羊土社．2002.
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