title: New paradigm in protein function prediction for large scale omics analysis.
abstract: Biological interpretation of large scale omics data, such as protein-protein interaction data and microarray gene expression data, requires that the function of many genes in a data set is annotated or predicted. Here the predicted function for a gene does not necessarily have to be a detailed biochemical function; a broad class of function, or low-resolution function, may be sufficient to understand why a set of genes shows the observed expression pattern or interaction pattern. In this Highlight, we focus on two recent approaches for function prediction which aim to provide large coverage in function prediction, namely omics data driven approaches and a thorough data mining approach on homology search results.
authors.affiliation: Department of Biological Sciences, College of Science, Purdue University, West Lafayette, IN 47907, USA.
authors.forename: Troy
authors.initials: T
authors.lastname: Hawkins
authors.forename: Meghana
authors.initials: M
authors.lastname: Chitale
authors.forename: Daisuke
authors.initials: D
authors.lastname: Kihara
mesh_terms: D000818:Animals; D019295:Computational Biology; D030541:Databases, Genetic; D008954:Models, Biological; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D017421:Sequence Analysis
chemical_list: D011506:Proteins