S. Imoto, SunYong Kim, Takao Goto, S. Aburatani, Kousuke Tashiro, S. Kuhara, S. Miyano
{"title":"遗传网络非线性建模的贝叶斯网络与非参数异方差回归。","authors":"S. Imoto, SunYong Kim, Takao Goto, S. Aburatani, Kousuke Tashiro, S. Kuhara, S. Miyano","doi":"10.1109/CSB.2002.1039344","DOIUrl":null,"url":null,"abstract":"We propose a new statistical method for constructing 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.","PeriodicalId":87204,"journal":{"name":"Proceedings. IEEE Computer Society Bioinformatics Conference","volume":"1 1","pages":"219-27"},"PeriodicalIF":0.0000,"publicationDate":"2002-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CSB.2002.1039344","citationCount":"17","resultStr":"{\"title\":\"Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network.\",\"authors\":\"S. Imoto, SunYong Kim, Takao Goto, S. Aburatani, Kousuke Tashiro, S. Kuhara, S. Miyano\",\"doi\":\"10.1109/CSB.2002.1039344\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a new statistical method for constructing 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.\",\"PeriodicalId\":87204,\"journal\":{\"name\":\"Proceedings. IEEE Computer Society Bioinformatics Conference\",\"volume\":\"1 1\",\"pages\":\"219-27\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/CSB.2002.1039344\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. IEEE Computer Society Bioinformatics Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSB.2002.1039344\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE Computer Society Bioinformatics Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSB.2002.1039344","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network.
We propose a new statistical method for constructing 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.