title: Computational identification of cellular networks and pathways.
abstract: In this article we highlight recent developments in computational functional genomics to identify networks of functionally related genes and proteins based on diverse sources of genomic data. Our specific focus is on statistical methods to identify genetic networks. We discuss integrated analysis of microarray datasets, methods to combine heterogeneous data sources, the analysis of high-dimensional phenotyping screens and describe efforts to establish a reliable and unbiased gold standard for method comparison and evaluation.
authors.affiliation: Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
authors.forename: Florian
authors.initials: F
authors.lastname: Markowetz
authors.forename: Olga G
authors.initials: OG
authors.lastname: Troyanskaya
mesh_terms: D019295:Computational Biology / Q000379:methods*; D023281:Genomics / Q000379:methods*; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods