title: An integrative multi-network and multi-classifier approach to predict genetic interactions.
abstract: Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date.
authors.affiliation: Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Minneapolis, Minnesota, United States of America.
authors.forename: Gaurav
authors.initials: G
authors.lastname: Pandey
authors.forename: Bin
authors.initials: B
authors.lastname: Zhang
authors.forename: Aaron N
authors.initials: AN
authors.lastname: Chang
authors.forename: Chad L
authors.initials: CL
authors.lastname: Myers
authors.forename: Jun
authors.initials: J
authors.lastname: Zhu
authors.forename: Vipin
authors.initials: V
authors.lastname: Kumar
authors.forename: Eric E
authors.initials: EE
authors.lastname: Schadt
mesh_terms: D019540:Area Under Curve; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D053263:Gene Regulatory Networks; D005800:Genes, Fungal; D010641:Phenotype; D012372:ROC Curve; D015203:Reproducibility of Results; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry / Q000145:classification / Q000235:genetics*; D018709:Statistics, Nonparametric; D014157:Transcription Factors / Q000737:chemistry / Q000145:classification / Q000235:genetics*
chemical_list: D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors