title: Predicting Mycobacterium tuberculosis complex clades using knowledge-based Bayesian networks.
abstract: We develop a novel approach for incorporating expert rules into Bayesian networks for classification of Mycobacterium tuberculosis complex (MTBC) clades. The proposed knowledge-based Bayesian network (KBBN) treats sets of expert rules as prior distributions on the classes. Unlike prior knowledge-based support vector machine approaches which require rules expressed as polyhedral sets, KBBN directly incorporates the rules without any modification. KBBN uses data to refine rule-based classifiers when the rule set is incomplete or ambiguous. We develop a predictive KBBN model for 69 MTBC clades found in the SITVIT international collection. We validate the approach using two testbeds that model knowledge of the MTBC obtained from two different experts and large DNA fingerprint databases to predict MTBC genetic clades and sublineages. These models represent strains of MTBC using high-throughput biomarkers called spacer oligonucleotide types (spoligotypes), since these are routinely gathered from MTBC isolates of tuberculosis (TB) patients. Results show that incorporating rules into problems can drastically increase classification accuracy if data alone are insufficient. The SITVIT KBBN is publicly available for use on the World Wide Web.
authors.affiliation: Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
authors.forename: Minoo
authors.identifier: 0000-0001-5204-1566
authors.initials: M
authors.lastname: Aminian
authors.affiliation: WHO Supranational TB Reference Laboratory, Tuberculosis and Mycobacteria Unit, Institut Pasteur de la Guadeloupe, 97183 Abymes, Guadeloupe, France.
authors.forename: David
authors.identifier: 0000-0002-7897-9023
authors.initials: D
authors.lastname: Couvin
authors.affiliation: Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
authors.forename: Amina
authors.identifier: 0000-0003-2308-3198
authors.initials: A
authors.lastname: Shabbeer
authors.affiliation: Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
authors.forename: Kane
authors.identifier: 0000-0002-2520-155X
authors.initials: K
authors.lastname: Hadley
authors.affiliation: Department of Computer Science, Siena College, Loudonville, NY 12180, USA.
authors.forename: Scott
authors.identifier: 0000-0003-0810-4544
authors.initials: S
authors.lastname: Vandenberg
authors.affiliation: WHO Supranational TB Reference Laboratory, Tuberculosis and Mycobacteria Unit, Institut Pasteur de la Guadeloupe, 97183 Abymes, Guadeloupe, France.
authors.forename: Nalin
authors.identifier: 0000-0002-7199-7747
authors.initials: N
authors.lastname: Rastogi
authors.affiliation: Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA ; Department of Mathematical Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
authors.forename: Kristin P
authors.initials: KP
authors.lastname: Bennett
mesh_terms: D001499:Bayes Theorem; D019992:Databases as Topic; D051188:Knowledge Bases; D009169:Mycobacterium tuberculosis / Q000145:classification*; D015203:Reproducibility of Results