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Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods

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 stati...

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Detalles Bibliográficos
Autores principales: Adler, Priit, Kolde, Raivo, Kull, Meelis, Tkachenko, Aleksandr, Peterson, Hedi, Reimand, Jüri, Vilo, Jaak
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2812946/
https://www.ncbi.nlm.nih.gov/pubmed/19961599
http://dx.doi.org/10.1186/gb-2009-10-12-r139
Descripción
Sumario: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/.