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Inference of Regulatory Gene Interactions from Expression Data Using Three‐Way Mutual Information
Author(s) -
Watkinson John,
Liang Kuoching,
Wang Xiadong,
Zheng Tian,
Anastassiou Dimitris
Publication year - 2009
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/j.1749-6632.2008.03757.x
Subject(s) - inference , pairwise comparison , computer science , context (archaeology) , computational biology , data mining , mutual information , set (abstract data type) , measure (data warehouse) , interaction information , expression (computer science) , machine learning , artificial intelligence , biology , mathematics , statistics , paleontology , programming language
This paper describes the technique designated best performer in the 2nd conference on Dialogue for Reverse Engineering Assessments and Methods (DREAM2) Challenge 5 (unsigned genome‐scale network prediction from blinded microarray data). Existing algorithms use the pairwise correlations of the expression levels of genes, which provide valuable but insufficient information for the inference of regulatory interactions. Here we present a computational approach based on the recently developed context likelihood of related (CLR) algorithm, extracting additional complementary information using the information theoretic measure of synergy and assigning a score to each ordered pair of genes measuring the degree of confidence that the first gene regulates the second. When tested on a set of publicly available Escherichia coli gene‐expression data with known assumed ground truth, the synergy augmented CLR (SA‐CLR) algorithm had significantly improved prediction performance when compared to CLR. There is also enhanced potential for biological discovery as a result of the identification of the most likely synergistic partner genes involved in the interactions.