iBBiG: iterative binary bi-clustering of gene sets | Zendy

Daniel Gusenleitner | Zendy; Eleanor Howe | Zendy; Stefan Bentink | Zendy; John Quackenbush | Zendy; Aedín C. Culhane | Zendy

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iBBiG: iterative binary bi-clustering of gene sets

Author(s) -

Daniel Gusenleitner,

Eleanor Howe,

Stefan Bentink,

John Quackenbush,

Aedín C. Culhane

Publication year - 2012

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bts438

Subject(s) - cluster analysis , bioconductor , computational biology , computer science , data set , set (abstract data type) , genomics , data mining , gene , biology , genome , genetics , artificial intelligence , programming language

Meta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets; however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods.

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