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Bayesian Network Approach to Estimate Gene Networks

Bayesian Network Approach to Estimate Gene Networks

Seiya Imoto, Satoru Miyano
Copyright: © 2007 |Pages: 31
ISBN13: 9781599041414|ISBN10: 1599041413|EISBN13: 9781599041438
DOI: 10.4018/978-1-59904-141-4.ch013
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MLA

Imoto, Seiya, and Satoru Miyano. "Bayesian Network Approach to Estimate Gene Networks." Bayesian Network Technologies: Applications and Graphical Models, edited by Ankush Mittal and Ashraf Kassim, IGI Global, 2007, pp. 269-299. https://doi.org/10.4018/978-1-59904-141-4.ch013

APA

Imoto, S. & Miyano, S. (2007). Bayesian Network Approach to Estimate Gene Networks. In A. Mittal & A. Kassim (Eds.), Bayesian Network Technologies: Applications and Graphical Models (pp. 269-299). IGI Global. https://doi.org/10.4018/978-1-59904-141-4.ch013

Chicago

Imoto, Seiya, and Satoru Miyano. "Bayesian Network Approach to Estimate Gene Networks." In Bayesian Network Technologies: Applications and Graphical Models, edited by Ankush Mittal and Ashraf Kassim, 269-299. Hershey, PA: IGI Global, 2007. https://doi.org/10.4018/978-1-59904-141-4.ch013

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Abstract

In cells, genes interact with each other and this system can be viewed as directed graphs. A gene network is a graphical representation of transcriptional relations between genes and the problem of estimation of gene networks from genome-wide data, such as DNA micro-array gene expression data, is one of the important issues in bioinformatics and systems biology. Here, we present a statistical method based on Bayesian networks to estimate gene networks from microarray data and other biological data. Because microarray data are measured as continuous variables and the relationship between genes are usually nonlinear, we combine Bayesian networks and nonparametric regression to handle continuous variables and nonlinear relations. Most parts of gene networks are still unknown, and we need to estimate them from observational data. This problem is equivalent to the structural learning of Bayesian networks, and we solve it from a Bayes approach. The main difficulty of gene network estimation is due to the number of genes involved in the network. Therefore, it leads to model overfitting to the observational data like microarray data. Hence, a combination of various kinds of biological data is a key technique to estimate accurate gene networks. We show a general framework to combine microarray data and other biological information to estimate gene networks.

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