title: Computational Prediction of the Global Functional Genomic Landscape: Applications, Methods, and Challenges.
abstract: Technological advances have led to an explosive growth of high-throughput functional genomic data. Exploiting the correlation among different data types, it is possible to predict one functional genomic data type from other data types. Prediction tools are valuable in understanding the relationship among different functional genomic signals. They also provide a cost-efficient solution to inferring the unknown functional genomic profiles when experimental data are unavailable due to resource or technological constraints. The predicted data may be used for generating hypotheses, prioritizing targets, interpreting disease variants, facilitating data integration, quality control, and many other purposes. This article reviews various applications of prediction methods in functional genomics, discusses analytical challenges, and highlights some common and effective strategies used to develop prediction methods for functional genomic data.
authors.affiliation: Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md., USA.
authors.forename: Weiqiang
authors.initials: W
authors.lastname: Zhou
authors.forename: Ben
authors.initials: B
authors.lastname: Sherwood
authors.forename: Hongkai
authors.initials: H
authors.lastname: Ji
mesh_terms: D002843:Chromatin / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D044127:Epigenesis, Genetic; D023281:Genomics / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D059467:Transcriptome / Q000235:genetics
chemical_list: D002843:Chromatin