Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network

S. Imoto, Kim Sunyong, T. Goto, S. Aburatani, Kosuke Tashiro, S. Kuhara, S. Miyano

研究成果: 著書/レポートタイプへの貢献会議での発言

15 引用 (Scopus)

抄録

We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. A problem still remains to be solved in selecting an optimal graph, which gives the best representation of the system among genes. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes.

元の言語英語
ホスト出版物のタイトルProceedings - IEEE Computer Society Bioinformatics Conference, CSB 2002
出版者Institute of Electrical and Electronics Engineers Inc.
ページ219-227
ページ数9
ISBN(電子版)076951653X, 9780769516530
DOI
出版物ステータス出版済み - 1 1 2002
イベント1st International IEEE Computer Society Bioinformatics Conference, CSB 2002 - Stanford, 米国
継続期間: 8 14 20028 16 2002

その他

その他1st International IEEE Computer Society Bioinformatics Conference, CSB 2002
米国
Stanford
期間8/14/028/16/02

Fingerprint

Bayesian networks
Gene expression
Genes
Microarrays
Gene Expression
Random variables
Yeast
Statistical methods
Patient Selection
Saccharomyces cerevisiae

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Biomedical Engineering
  • Health Informatics

これを引用

Imoto, S., Sunyong, K., Goto, T., Aburatani, S., Tashiro, K., Kuhara, S., & Miyano, S. (2002). Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. : Proceedings - IEEE Computer Society Bioinformatics Conference, CSB 2002 (pp. 219-227). [1039344] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSB.2002.1039344

Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. / Imoto, S.; Sunyong, Kim; Goto, T.; Aburatani, S.; Tashiro, Kosuke; Kuhara, S.; Miyano, S.

Proceedings - IEEE Computer Society Bioinformatics Conference, CSB 2002. Institute of Electrical and Electronics Engineers Inc., 2002. p. 219-227 1039344.

研究成果: 著書/レポートタイプへの貢献会議での発言

Imoto, S, Sunyong, K, Goto, T, Aburatani, S, Tashiro, K, Kuhara, S & Miyano, S 2002, Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. : Proceedings - IEEE Computer Society Bioinformatics Conference, CSB 2002., 1039344, Institute of Electrical and Electronics Engineers Inc., pp. 219-227, 1st International IEEE Computer Society Bioinformatics Conference, CSB 2002, Stanford, 米国, 8/14/02. https://doi.org/10.1109/CSB.2002.1039344
Imoto S, Sunyong K, Goto T, Aburatani S, Tashiro K, Kuhara S その他. Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. : Proceedings - IEEE Computer Society Bioinformatics Conference, CSB 2002. Institute of Electrical and Electronics Engineers Inc. 2002. p. 219-227. 1039344 https://doi.org/10.1109/CSB.2002.1039344
Imoto, S. ; Sunyong, Kim ; Goto, T. ; Aburatani, S. ; Tashiro, Kosuke ; Kuhara, S. ; Miyano, S. / Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. Proceedings - IEEE Computer Society Bioinformatics Conference, CSB 2002. Institute of Electrical and Electronics Engineers Inc., 2002. pp. 219-227
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