title: Prediction of oral cancer recurrence using dynamic Bayesian networks.
abstract: We propose a methodology for predicting oral cancer recurrence using Dynamic Bayesian Networks. The methodology takes into consideration time series gene expression data collected at the follow-up study of patients that had or had not suffered a disease relapse. Based on that knowledge, our aim is to infer the corresponding dynamic Bayesian networks and subsequently conjecture about the causal relationships among genes within the same time-slice and between consecutive time-slices. Moreover, the proposed methodology aims to (i) assess the prognosis of patients regarding oral cancer recurrence and at the same time, (ii) provide important information about the underlying biological processes of the disease.
authors.forename: Konstantina
authors.initials: K
authors.lastname: Kourou
authors.forename: George
authors.initials: G
authors.lastname: Rigas
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: D000465:Algorithms; D001499:Bayes Theorem; D030541:Databases, Genetic; D053263:Gene Regulatory Networks; D006801:Humans; D009062:Mouth Neoplasms / Q000235:genetics / Q000473:pathology*; D009364:Neoplasm Recurrence, Local / Q000473:pathology*; D012372:ROC Curve