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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. 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 2007, 1:39 (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., 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. In press. 6. 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. 7. Imoto, S. Knowledge discovery of causal relations among genes from microarray gene expression data, Journal of Japan Statistical Society. 37(1): 55-70, 2007. 8. 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. 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. 10. Jeong, E., Nagasai, M., Saito, A., Miyano, S. Cell System Ontology: Representation for modeling, visualizing, and simulating biological pathways. In Silico Biology 7, 0055, 2007. 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. 8:76, 2007. 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, in press, 2007. 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- 21. Ando, T., Konishi, S., Imoto, S. Nonlinear regression modeling via regularized radial basis function networks. J. Statistical Planning and Inference. In press. 22. 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. 23. 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. 24. 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. 25. 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. 26. 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. 27. 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. Bioinformatics and Computional Biology. 4(5):1119-1140, 2006. 28. 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. Bioinformatics and Computional Biology. 4(1): 139-154, 2006. 29. 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. 30. 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. 31. 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. 32. 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. 33. 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. 34. 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. 35. 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. 36. 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. 37. 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. 38. Washio, T., Higuchi, T., Imoto, S., Tamada, Y., Sato, K., Motoda, H. Graph mining and its application to statistical modeling. Proc. Inst. Statist. Math. 54(2): 315-332, 2006. 39. 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. 40. 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 - 41. 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. 42. 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. 43. 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. 44. 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. 45. Li, C., Suzuki, S., Ge, Q.-W., Nakata, M., Matsuno, H., Miyano, S. On modeling and analyzing signaling pathways with inhibitory interactions based on Petri net. BIOINFO2005, 348-353, 2005. 46. 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. 47. Nagasaki, M., Doi, A., Matsuno, H., Miyano, S. Petri net modeling of biological pathways. Proc. Algebraic Biology 2005, Universal Academy Press, 19-31, 2005. 48. 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. 49. 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. 50. 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. 51. 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. 52. 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. 53. Yoshida, R., Higuchi, T., Imoto, S. Estimating time-dependent gene networks from time series DNA microarray data by dynamic linear model with Markov switching. Proc. IEEE 4th Computational Systems Bioinformatics. IEEE Press. 289-298, 2005. 54. Yoshida, R., Imoto, S., Higuchi, T. A penalized likelihood estimation on transcriptional module-based clustering. Lecture Notes in Computer Science. 3482: 389-401, 2005.

- 2004 - 55. 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. 56. 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. 57. 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. 58. 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. 59. 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. 60. 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. 61. 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. 62. 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. 63. 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. 64. De Hoon, M.J.L., Imoto, S., Nolan, J., Miyano, S. Open source clustering software. Bioinformatics. 20(9):1453-1454, 2004. 65. 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. 66. 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. 67. 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. 68. 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. 69. 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. 70. 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. 71. 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. 72. 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. 73. Konishi, S., Ando, T., Imoto, S. Bayesian information criteria and smoothing parameter selection in radial basis function networks. Biometrika. 91: 27-43, 2004. 74. 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.) 75. Miyano, S. Computational systems biology. Proc. Third International Conference on Information (Li, L. and Yen, K.K., Eds.). 9-14, 2004. 76. 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. 77. 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. 78. 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. 79. 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. 80. 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. 81. Ott, S., Imoto, S., Miyano, S. Finding optimal models for small gene networks. Pacific Symposium on Biocomputing. 9:557-567, 2004. 82. 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. 83. 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 - 84. 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. 85. 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. 86. Bannai, H., Inenaga, S., Shinohara, A., Takeda, M., Inferring strings from graphs and arrays. Lecture Notes in Computer Science. 2747: 208-217, 2003. 87. 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. 88. 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. 89. 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. 90. 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. 91. 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. 92. 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. 93. 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. 94. 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. 95. Matsuno, H., Fujita, S., Doi, A., Nagasaki, M., Miyano, S. Towards biopathway modeling and simulation. Lecture Notes in Computer Science. 2679:3-22, 2003. 96. Miyano, S. Inference, modeling and simulation of gene networks. Lecture Notes in Computer Science. 2602:207-211, 2003. 97. 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. 98. Nagasaki, M., Doi, A., Matsuno, H., Miyano, S. Recreating biopathway databases towards simulation. Lecture Notes in Computer Science. 2602:191-192, 2003. 99. 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. 100. Ott, S., Tamada, Y., Bannai, H., Nakai, K., Miyano, S. Intrasplicing - analysis of long intron sequences. Pacific Symposium on Biocomputing. 8:339-350, 2003. 101. Ott, S., Miyano, S. Finding optimal gene networks using biological constraints. Genome Informatics. 14:124-133, 2003. 102. 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. 103. 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. 104. 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. 105. 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. 106. 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 - 107. 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. 108. Akutsu, T., Ott, S. Inferring a union of halfspaces from examples. Lecture Notes in Computer Science, 2387:117-126, 2002. 109. 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. 110. 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. 111. 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. 112. 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. 113. 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. 114. 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. 115. 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. 116. 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. 117. 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. 118. Sumii, E., Bannai, H. VM lambda: a functional calculusfor scientific discovery. Lecture Notes in Computer Science, 2441:290-304, 2002. 119. 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. 120. 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 121. 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) 122. 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). 123. 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) 124. 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. Heinrich, T., DeLisi, C., Kanehisa, M., Miyano, S. (Eds.). Genome Informatics. 16 (1), 2005. 6. Heinrich, R., Mamitsuka, H., Kanehisa, M., Miyano, S., Takagi, T. (Eds.). Genome Informatics. 16 (2), 2005. 7. 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. 8. Akutsu, T., Brusic, V., Miyano, S., Takagi, T., Kanehisa, M. (Eds.). Genome Informatics. 15(2), 2004. 9. Mamitsuka, H., Smith, T.F., Holzhütter, H-G., Kanehisa, M., DeLisi, C., Heinrich, R., Miyano, S. (Eds.). Genome Informatics. 15(1), 2004. 10. Gribskov, M., Kanehisa, M., Miyano, S., Takagi, T. (Eds.). Genome Informatics. 14, 2003. 11. Lathrop, R., Nakai, K., Miyano, S., Takagi, T., Kanehisa, M. (Eds.). Genome Informatics. 13, 2002.