title: Review on statistical methods for gene network reconstruction using expression data.
abstract: Network modeling has proven to be a fundamental tool in analyzing the inner workings of a cell. It has revolutionized our understanding of biological processes and made significant contributions to the discovery of disease biomarkers. Much effort has been devoted to reconstruct various types of biochemical networks using functional genomic datasets generated by high-throughput technologies. This paper discusses statistical methods used to reconstruct gene regulatory networks using gene expression data. In particular, we highlight progress made and challenges yet to be met in the problems involved in estimating gene interactions, inferring causality and modeling temporal changes of regulation behaviors. As rapid advances in technologies have made available diverse, large-scale genomic data, we also survey methods of incorporating all these additional data to achieve better, more accurate inference of gene networks.
authors.affiliation: Department of Statistics, University of California, Berkeley, CA 94720, USA. Electronic address: rachelwang@stat.berkeley.edu.
authors.forename: Y X Rachel
authors.initials: YX
authors.lastname: Wang
authors.affiliation: Department of Statistics, University of California, Berkeley, CA 94720, USA. Electronic address: hhuang@stat.berkeley.edu.
authors.forename: Haiyan
authors.initials: H
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D015415:Biomarkers / Q000378:metabolism; D016000:Cluster Analysis; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D023281:Genomics; D006801:Humans; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D012984:Software
chemical_list: D015415:Biomarkers
keywords: Bayesian networks; Coexpression networks; Community detection; Dynamic networks; Genomic data integration