Genetic interaction motif finding by expectation maximization - a novel statistical model for inferring gene modules from synthe

Background: Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex) may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. Probabilistic algorithms that identify gene modules based on motif discovery are highly appropriate for the analysis of synthetic lethal genetic interaction data and have great potential in integrative analysis of heterogeneous datasets. Results: We have developed Genetic Interaction Motif Finding (GIMF), an algorithm for unsupervised motif discovery from synthetic lethal interaction data. Interaction motifs are characterized by position weight matrices and optimized through expectation maximization. Given a seed gene, GIMF performs a nonlinear transform on the input genetic interaction data and automatically assigns genes to the motif or non-motif categor...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Yan Qi 0003, Ping Ye, Joel S. Bader
• Keywords: BMCBI 2005, Genetic Interaction, Interaction Data, Synthetic Lethal Interactions