Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks | Zendy

Antônio Reverter | Zendy; Eva K.F. Chan | Zendy

Open Access

Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks

Author(s) -

Antônio Reverter,

Eva K.F. Chan

Publication year - 2008

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btn482

Subject(s) - context (archaeology) , cutoff , correlation , source code , cluster analysis , computer science , data mining , correlation coefficient , code (set theory) , gene , algorithm , relation (database) , computational biology , mathematics , biology , artificial intelligence , machine learning , genetics , physics , paleontology , geometry , set (abstract data type) , quantum mechanics , programming language , operating system

We present PCIT, an algorithm for the reconstruction of gene co-expression networks (GCN) that combines the concept partial correlation coefficient with information theory to identify significant gene to gene associations defining edges in the reconstruction of GCN. The properties of PCIT are examined in the context of the topology of the reconstructed network including connectivity structure, clustering coefficient and sensitivity.

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