title: Exploiting big biology: integrating large-scale biological data for function inference.
abstract: The amount of data produced by molecular biologists is growing at an exponential rate. Some of the fastest growing sets of data are measurements of gene expression, comparable in quantity only to gene sequences and the vast biological literature. Both gene expression data and sequence data offer hints as to the functions of thousands of newly discovered genes, but neither give complete answers. Therefore, much effort is being focused on integrating these large data sets and combining them with all available functional data to draw inferences about the functions of uncharacterised genes. This review discusses the most pertinent functional data for genome-wide functional inference and describes several methods by which these disparate data types are being integrated.
authors.affiliation: Department of Chemistry and Biochemistry and Institute for Cellular and Molecular Biology, University of Texas at Austin, 78712, USA. marcotte@icmb.utexas.edu
authors.forename: E
authors.initials: E
authors.lastname: Marcotte
authors.forename: S
authors.initials: S
authors.lastname: Date
mesh_terms: D003625:Data Collection; D003627:Data Interpretation, Statistical; D030561:Databases, Nucleic Acid / Q000639:trends; D030562:Databases, Protein / Q000639:trends; D023281:Genomics / Q000639:trends; D016247:Information Storage and Retrieval; D017422:Sequence Analysis, DNA