Reconciling differential gene expression data with molecular interaction networks | Zendy

Christopher L. Poirel | Zendy; Ahsanur Rahman | Zendy; Richard R. Rodrigues | Zendy; Arjun Krishnan | Zendy; Jacqueline R. Addesa | Zendy; T. M. Murali | Zendy

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Reconciling differential gene expression data with molecular interaction networks

Author(s) -

Christopher L. Poirel,

Ahsanur Rahman,

Richard R. Rodrigues,

Arjun Krishnan,

Jacqueline R. Addesa,

T. M. Murali

Publication year - 2013

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/btt007

Subject(s) - compendium , computer science , software , dna microarray , computational biology , class (philosophy) , expression (computer science) , data mining , gene expression , gene , differential (mechanical device) , spurious relationship , microarray analysis techniques , bioinformatics , biology , machine learning , artificial intelligence , genetics , archaeology , engineering , history , programming language , aerospace engineering

Many techniques have been developed to compute the response network of a cell. A recent trend in this area is to compute response networks of small size, with the rationale that only part of a pathway is often changed by disease and that interpreting small subnetworks is easier than interpreting larger ones. However, these methods may not uncover the spectrum of pathways perturbed in a particular experiment or disease.

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