title: Complementary gene signature integration in multiplatform microarray experiments.
abstract: The concept of gene signature overlap has been addressed previously in a number of research papers. A common conclusion is the absence of significant overlap. In this paper, we verify the aforementioned fact, but we also assess the issue of similarities not on the gene level, but on the biology level hidden underneath a given signature. We proceed by taking into account the biological knowledge that exists among different signatures, and use it as a means of integrating them and refining their statistical significance on the datasets. In this form, by integrating biological knowledge with information stemming from data distributions, we derive a unified signature that is significantly improved over its predecessors in terms of performance and robustness. Our motive behind this approach is to assess the problem of evaluating different signatures not in a competitive but rather in a complementary manner, where one is treated as a pool of knowledge contributing to a global and unified solution.
authors.affiliation: Department of Electronic and Computer Engineering, Technical University of Crete, Chania 73100, Greece. mblazad@gmail.com
authors.forename: Michalis E
authors.initials: ME
authors.lastname: Blazadonakis
authors.forename: Michalis E
authors.initials: ME
authors.lastname: Zervakis
authors.forename: Dimitris
authors.initials: D
authors.lastname: Kafetzopoulos
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000235:genetics*; D001943:Breast Neoplasms / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030561:Databases, Nucleic Acid; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D053208:Kaplan-Meier Estimate; D020411:Oligonucleotide Array Sequence Analysis; D012372:ROC Curve
chemical_list: D014408:Biomarkers, Tumor