Year: 2019
Type: article
Abstract: MOTIVATION: Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. RESULTS: We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space. AVAILABILITY AND IMPLEMENTATION: The details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Source: Bioinformatics
Authors: Yang Yang, Timothy M Walker, A Sarah Walker, Daniel J Wilson, Timothy E A Peto, Derrick W Crook, Farah Shamout, Irena Arandjelovic, Iñaki Comas, Maha R Farhat, Qian Gao, Vitali Sintchenko, Dick van Soolingen, Sarah Hoosdally, Ana L Gibertoni Cruz, Joshua Carter, Clara Grazian, Sarah G Earle, Samaneh Kouchaki, Yang Yang, Timothy M Walker, Philip W Fowler, David A Clifton, Zamin Iqbal, Martin Hunt, E Grace Smith, Priti Rathod, Lisa Jarrett, Daniela Matias, Daniela M Cirillo, Emanuele Borroni, Simone Battaglia, Arash Ghodousi, Andrea Spitaleri, Andrea Cabibbe, Sabira Tahseen, Kayzad Nilgiriwala, Sanchi Shah, Camilla Rodrigues, Priti Kambli, Utkarsha Surve, Rukhsar Khot, Stefan Niemann, Thomas Kohl, Matthias Merker, Harald Hoffmann, Nikolay Molodtsov, Sara Plesnik, Nazir Ismail, Guy Thwaites, Thuong Nguyen Thuy Thuong, Nhung Hoang Ngoc, Vijay Srinivasan, David Moore, David Jorge Coronel, Walter Solano, George F Gao, Guangxue He, Yanlin Zhao, Aijing Ma, Chunfa Liu, Baoli Zhu, Ian Laurenson, Pauline Claxton, Anastasia Koch, Robert Wilkinson, Ajit Lalvani, James Posey, James Jennifer Gardy, Jim Werngren, Nicholas Paton, Ruwen Jou, Mei-Hua Wu, Wan-Hsuan Lin, Lucilaine Ferrazoli, Rosangela Siqueira de Oliveira, São Paulo, Tingting Zhu, David A Clifton
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $2,269
Funders: Bill and Melinda Gates Foundation, London School of Hygiene and Tropical Medicine, Wellcome Trust, National Institute for Health and Care Research, Royal Academy of Engineering, Royal Society, Imperial College London, University of Oxford, National University of Singapore, Public Health Agency, Public Health England, Fondation pour la Recherche Médicale, Chinese Center for Disease Control and Prevention, University of Cape Town, Folkhälsomyndigheten, Centers for Disease Control and Prevention, Medical Research Council, Engineering and Physical Sciences Research Council, National Institute of Environmental Health Sciences
Awards: G1198837610, G1628705071, NIHR Senior Investigator Award, NIHR Senior Investigator Award, Host directed therapies against tuberculosis., Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC), New Tools for the interpretation of Pathogen Genomic Data with a focus on Mycobacterium tuberculosis, Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC), Statistical Methods for Whole Genome Phenotype Mapping in Bacterial Populations.
OpenAlex ID: W2928080709