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| - | ====== 2004 ====== | ||
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| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - De Hoon, M.J.L., Imoto, S., Nolan, J., Miyano, S. Open source clustering software. Bioinformatics. 20(9):1453-1454, 2004. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - Konishi, S., Ando, T., Imoto, S. Bayesian information criteria and smoothing parameter selection in radial basis function networks. Biometrika. 91: 27-43, 2004. | ||
| - | - 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.) | ||
| - | - Miyano, S. Computational systems biology. Proc. Third International Conference on Information (Li, L. and Yen, K.K., Eds.). 9-14, 2004. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - 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. | ||
| - | - Ott, S., Imoto, S., Miyano, S. Finding optimal models for small gene networks. Pacific Symposium on Biocomputing. 9:557-567, 2004. | ||
| - | - 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. | ||
| - | - 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. | ||
