Generalized empirical Bayesian methods for discovery of differential data in high-throughput biology | Zendy

Thomas J. Hardcastle | Zendy

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Generalized empirical Bayesian methods for discovery of differential data in high-throughput biology

Author(s) -

Thomas J. Hardcastle

Publication year - 2015

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/btv569

Subject(s) - bayesian probability , computer science , throughput , computational biology , differential (mechanical device) , data mining , biology , artificial intelligence , physics , telecommunications , wireless , thermodynamics

High-throughput data are now commonplace in biological research. Rapidly changing technologies and application mean that novel methods for detecting differential behaviour that account for a 'large P, small n' setting are required at an increasing rate. The development of such methods is, in general, being done on an ad hoc basis, requiring further development cycles and a lack of standardization between analyses.

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