Skip to Main content Skip to Navigation
New interface
Journal articles

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.
Document type :
Journal articles
Complete list of metadata

Cited literature [19 references]  Display  Hide  Download
Contributor : Nalin Rastogi Connect in order to contact the contributor
Submitted on : Sunday, June 22, 2014 - 11:11:37 PM
Last modification on : Monday, October 8, 2018 - 5:44:05 PM
Long-term archiving on: : Tuesday, April 11, 2017 - 8:06:56 AM


Publisher files allowed on an open archive




Minoo Aminian, David Couvin, Amina Shabbeer, Kane Hadley, Scott Vandenberg, et al.. Predicting Mycobacterium tuberculosis Complex Clades Using Knowledge-Based Bayesian Networks.. BioMed Research International , 2014, 2014, Article ID 398484, 11 p. ⟨10.1155/2014/398484⟩. ⟨pasteur-01011081⟩



Record views


Files downloads