title: Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods.
abstract: We present a web resource MEM (Multi-Experiment Matrix) for gene expression similarity searches across many datasets. MEM features large collections of microarray datasets and utilizes rank aggregation to merge information from different datasets into a single global ordering with simultaneous statistical significance estimation. Unique features of MEM include automatic detection, characterization and visualization of datasets that includes the strongest coexpression patterns. MEM is freely available at http://biit.cs.ut.ee/mem/.
authors: Institute of Molecular and Cell Biology, Riia 23, 51010 Tartu, Estonia. adler@ut.ee
authors: Priit
authors: P
authors: Adler
authors: Raivo
authors: R
authors: Kolde
authors: Meelis
authors: M
authors: Kull
authors: Aleksandr
authors: A
authors: Tkachenko
authors: Hedi
authors: H
authors: Peterson
authors: Jüri
authors: J
authors: Reimand
authors: Jaak
authors: J
authors: Vilo
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020407:Internet; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D012984:Software