title: Inferring cancer subnetwork markers using density-constrained biclustering.
abstract: BACKGROUND
  Recent genomic studies have confirmed that cancer is of utmost phenotypical complexity, varying greatly in terms of subtypes and evolutionary stages. When classifying cancer tissue samples, subnetwork marker approaches have proven to be superior over single gene marker approaches, most importantly in cross-platform evaluation schemes. However, prior subnetwork-based approaches do not explicitly address the great phenotypical complexity of cancer.
  
  
  RESULTS
  We explicitly address this and employ density-constrained biclustering to compute subnetwork markers, which reflect pathways being dysregulated in many, but not necessarily all samples under consideration. In breast cancer we achieve substantial improvements over all cross-platform applicable approaches when predicting TP53 mutation status in a well-established non-cross-platform setting. In colon cancer, we raise prediction accuracy in the most difficult instances from 87% to 93% for cancer versus non-cancer and from 83% to (astonishing) 92%, for with versus without liver metastasis, in well-established cross-platform evaluation schemes.
  
  
  BACKGROUND
  Software is available on request.
authors.affiliation: School of Computing Science, Simon Fraser University, Burnaby, Canada.
authors.forename: Phuong
authors.initials: P
authors.lastname: Dao
authors.forename: Recep
authors.initials: R
authors.lastname: Colak
authors.forename: Raheleh
authors.initials: R
authors.lastname: Salari
authors.forename: Flavia
authors.initials: F
authors.lastname: Moser
authors.forename: Elai
authors.initials: E
authors.lastname: Davicioni
authors.forename: Alexander
authors.initials: A
authors.lastname: Schönhuth
authors.forename: Martin
authors.initials: M
authors.lastname: Ester
mesh_terms: D000465:Algorithms; D019985:Benchmarking; D014408:Biomarkers, Tumor; D001943:Breast Neoplasms / Q000235:genetics; D003110:Colonic Neoplasms / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D016158:Genes, p53; D006801:Humans; D009369:Neoplasms / Q000145:classification / Q000235:genetics*; D012984:Software
chemical_list: D014408:Biomarkers, Tumor
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