title: Significance and Functional Similarity for Identification of Disease Genes.
abstract: One of the most significant research issues in functional genomics is insilico identification of disease related genes. In this regard, the paper presents a new gene selection algorithm, termed as SiFS, for identification of disease genes. It integrates the information obtained from interaction network of proteins and gene expression profiles. The proposed SiFS algorithm culls out a subset of genes from microarray data as disease genes by maximizing both significance and functional similarity of the selected gene subset. Based on the gene expression profiles, the significance of a gene with respect to another gene is computed using mutual information. On the other hand, a new measure of similarity is introduced to compute the functional similarity between two genes. Information derived from the protein-protein interaction network forms the basis of the proposed SiFS algorithm. The performance of the proposed gene selection algorithm and new similarity measure, is compared with that of other related methods and similarity measures, using several cancer microarray data sets.
authors.forename: Pradipta
authors.initials: P
authors.lastname: Maji
authors.forename: Ekta
authors.initials: E
authors.lastname: Shah
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D060066:Protein Interaction Maps / Q000235:genetics*