title: Integrating Multiple Data Sources for Combinatorial Marker Discovery: A Study in Tumorigenesis.
abstract: Identification of combinatorial markers from multiple data sources is a challenging task in bioinformatics. Here, we propose a novel computational framework for identifying significant combinatorial markers ( s) using both gene expression and methylation data. The gene expression and methylation data are integrated into a single continuous data as well as a (post-discretized) boolean data based on their intrinsic (i.e., inverse) relationship. A novel combined score of methylation and expression data (viz., ) is introduced which is computed on the integrated continuous data for identifying initial non-redundant set of genes. Thereafter, (maximal) frequent closed homogeneous genesets are identified using a well-known biclustering algorithm applied on the integrated boolean data of the determined non-redundant set of genes. A novel sample-based weighted support ( ) is then proposed that is consecutively calculated on the integrated boolean data of the determined non-redundant set of genes in order to identify the non-redundant significant genesets. The top few resulting genesets are identified as potential s. Since our proposed method generates a smaller number of significant non-redundant genesets than those by other popular methods, the method is much faster than the others. Application of the proposed technique on an expression and a methylation data for Uterine tumor or Prostate Carcinoma produces a set of significant combination of markers. We expect that such a combination of markers will produce lower false positives than individual markers.
authors.forename: Sanghamitra
authors.initials: S
authors.lastname: Bandyopadhyay
authors.forename: Saurav
authors.initials: S
authors.lastname: Mallik
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics*; D063646:Carcinogenesis / Q000235:genetics*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D019175:DNA Methylation; D030541:Databases, Genetic; D015870:Gene Expression; D006801:Humans
chemical_list: D014408:Biomarkers, Tumor