title: Genome-wide functional annotation by integrating multiple microarray datasets using meta-analysis.
abstract: Tremendous amounts of microarray data for various organisms have provided a rich opportunity for computational analyses of gene products. Integrating these data can help inferring biological knowledge effectively. We present a new statistical method of integrating multiple microarray datasets for gene function prediction. We tested the performance of our model using yeast and human datasets. Our results show that combining multiple datasets improves the accuracy over the best function prediction of any single dataset significantly. We also compared performance of the meta p-value and meta correlation methods for function prediction. Supplementary results and code are available at http://digbio.missouri.edu/metaanalyses.
authors.affiliation: Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri-Columbia, 1201 E, Rollins Rd. Columbia, MO 65201, USA. gps8b9@mizzou.edu
authors.forename: Gyan Prakash
authors.initials: GP
authors.lastname: Srivastava
authors.forename: Jing
authors.initials: J
authors.lastname: Qiu
authors.forename: Dong
authors.initials: D
authors.lastname: Xu
mesh_terms: D020869:Gene Expression Profiling / Q000379:methods*; D016678:Genome; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015003:Yeasts / Q000235:genetics