ArticleBioMed research international2014
Predicting Mycobacterium tuberculosis complex clades using knowledge-based Bayesian networks.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Artificial Intelligence in Bacterial Infections Control: A Scoping Review.Antibiotics (Basel, Switzerland) · 2025Review
- Genotypes and drug resistance pattern ofFrontiers in public health · 2024Article
- Genetic diversity ofHeliyon · 2023Article
- Novel katG mutations causing isoniazid resistance in clinical M. tuberculosis isolates.Emerging microbes & infections · 2015Article
- Phenotypic and genotypic diversity in a multinational sample of drug-resistant Mycobacterium tuberculosis isolates.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2015Article
- High Genotypic Discordance of Concurrent Mycobacterium tuberculosis Isolates from Sputum and Blood of HIV-Infected Individuals.PloS one · 2015Article
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Authors and funding
7 authors.
Funding
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.
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Registered trials
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