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Discovering functional gene pathways associated with cancer heterogeneity via sparse supervised learning.

S. Kawano, T. Shimamura, A. Niida, S. Imoto, R. Yamaguchi, M. Nagasaki, R. Yoshida, C. Print, and S. Miyano. BIBM, page 253-258. IEEE Computer Society, (2010)

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A Bayesian model integration for mutation calling through data partitioning.T. Moriyama, S. Imoto, S. Hayashi, Y. Shiraishi, S. Miyano, and R. Yamaguchi. Bioinform., 35 (21): 4247-4254 (2019)Utilizing Evolutionary Information and Gene Expression Data for Estimating Gene Networks with Bayesian Network Models.Y. Tamada, H. Bannai, S. Imoto, T. Katayama, M. Kanehisa, and S. Miyano. J. Bioinform. Comput. Biol., 3 (6): 1295-1314 (2005)Interaction-Based Feature Selection for Uncovering Cancer Driver Genes Through Copy Number-Driven Expression Level.H. Park, A. Niida, S. Imoto, and S. Miyano. J. Comput. Biol., 24 (2): 138-152 (2017)Protein Fold Recognition Using Genetic Algorithm Optimized Voting Scheme and Profile Bigram.H. Saini, G. Raicar, S. Lal, A. Dehzangi, S. Imoto, and A. Sharma. J. Softw., 11 (8): 756-767 (2016)Inferring dynamic gene networks under varying conditions for transcriptomic network comparison.T. Shimamura, S. Imoto, R. Yamaguchi, M. Nagasaki, and S. Miyano. Bioinform., 26 (8): 1064-1072 (2010)Model-free unsupervised gene set screening based on information enrichment in expression profiles.A. Niida, S. Imoto, R. Yamaguchi, M. Nagasaki, A. Fujita, T. Shimamura, and S. Miyano. Bioinform., 26 (24): 3090-3097 (2010)iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities.J. Xu, F. Li, C. Li, X. Guo, C. Landersdorfer, H. Shen, A. Peleg, J. Li, S. Imoto, J. Yao and 2 other author(s). Briefings Bioinform., (July 2023)Targeting tumor heterogeneity: multiplex-detection-based multiple instance learning for whole slide image classification.Z. Wang, Y. Bi, T. Pan, X. Wang, C. Bain, R. Bassed, S. Imoto, J. Yao, R. Daly, and J. Song. Bioinform., (March 2023)Identifying Gene Pathways Associated with Cancer Characteristics via Sparse Statistical Methods.S. Kawano, T. Shimamura, A. Niida, S. Imoto, R. Yamaguchi, M. Nagasaki, R. Yoshida, C. Print, and S. Miyano. IEEE ACM Trans. Comput. Biol. Bioinform., 9 (4): 966-972 (2012)Estimating Genome-Wide Gene Networks Using Nonparametric Bayesian Network Models on Massively Parallel Computers.Y. Tamada, S. Imoto, H. Araki, M. Nagasaki, C. Print, S. Charnock-Jones, and S. Miyano. IEEE ACM Trans. Comput. Biol. Bioinform., 8 (3): 683-697 (2011)

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Disambiguation of "Imoto, Seiya"

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Discovering functional gene pathways associated with cancer heterogeneity via sparse supervised learning.

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Disambiguation of "Imoto, Seiya"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Discovering functional gene pathways associated with cancer heterogeneity via sparse supervised learning.

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Other publications of authors with the same name

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Discovering functional gene pathways associated with cancer heterogeneity via sparse supervised learning.