maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments | Zendy

Ana Conesa | Zendy; María José Nueda | Zendy; Alberto Ferrer | Zendy; Manuel Talón | Zendy

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maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments

Author(s) -

Ana Conesa,

María José Nueda,

Alberto Ferrer,

Manuel Talón

Publication year - 2006

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/btl056

Subject(s) - microarray analysis techniques , microarray , regression analysis , expression (computer science) , regression , computer science , differential (mechanical device) , data mining , time point , variables , computational biology , gene expression , statistics , biology , gene , mathematics , machine learning , genetics , engineering , programming language , aerospace engineering , philosophy , aesthetics

Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis.

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