title: A Bayesian Network-based approach for discovering oral cancer candidate biomarkers.
abstract: Oral cancer can arise in the head and neck region. Due to the aggressive nature of the disease, which often leads to poor prognosis, Oral Squamous Cell Carcinoma (OSCC) constitutes the 8(th) most common neoplasms in humans. In the present work we formulate gene interaction network from oral cancer genomic data using Dynamic Bayesian Networks (DBNs). Four modules were extracted after applying a clustering technique to the network. We consequently explore them by applying topological and functional analysis methods in order to identify significant network nodes. Our analysis revealed that these important nodes may correspond to candidate biomarkers of the disease.
authors.forename: Konstantina
authors.initials: K
authors.lastname: Kourou
authors.forename: Konstantinos P
authors.initials: KP
authors.lastname: Exarchos
authors.forename: Costas
authors.initials: C
authors.lastname: Papaloukas
authors.forename: Dimitrios I
authors.initials: DI
authors.lastname: Fotiadis
mesh_terms: D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000235:genetics*; D002294:Carcinoma, Squamous Cell / Q000235:genetics* / Q000473:pathology; D016208:Databases, Factual; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D023281:Genomics / Q000379:methods; D006801:Humans; D009062:Mouth Neoplasms / Q000235:genetics* / Q000473:pathology
chemical_list: D014408:Biomarkers, Tumor