title: Bioinformatics/biostatistics: microarray analysis.
abstract: The quantity and complexity of the molecular-level data generated in both research and clinical settings require the use of sophisticated, powerful computational interpretation techniques. It is for this reason that bioinformatic analysis of complex molecular profiling data has become a fundamental technology in the development of personalized medicine. This chapter provides a high-level overview of the field of bioinformatics and outlines several, classic bioinformatic approaches. The highlighted approaches can be aptly applied to nearly any sort of high-dimensional genomic, proteomic, or metabolomic experiments. Reviewed technologies in this chapter include traditional clustering analysis, the Gene Expression Dynamics Inspector (GEDI), GoMiner (GoMiner), Gene Set Enrichment Analysis (GSEA), and the Learner of Functional Enrichment (LeFE).
authors.affiliation: InnoCentive Inc., Waltham, MA, USA. gabeeichler@gmail.com
authors.forename: Gabriel S
authors.initials: GS
authors.lastname: Eichler
mesh_terms: D000465:Algorithms; D056808:Biostatistics / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D023281:Genomics / Q000379:methods; D006801:Humans; D046228:Microarray Analysis / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software