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- Title
A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis.
- Authors
Green, Anna G.; Yoon, Chang Ho; Chen, Michael L.; Ektefaie, Yasha; Fina, Mack; Freschi, Luca; Gröschel, Matthias I.; Kohane, Isaac; Beam, Andrew; Farhat, Maha
- Abstract
Long diagnostic wait times hinder international efforts to address antibiotic resistance in M. tuberculosis. Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution. However, generalizability and clinical adoption have been limited by a lack of interpretability, especially in deep learning methods. Here, we present two deep convolutional neural networks that predict antibiotic resistance phenotypes of M. tuberculosis isolates: a multi-drug CNN (MD-CNN), that predicts resistance to 13 antibiotics based on 18 genomic loci, with AUCs 82.6-99.5% and higher sensitivity than state-of-the-art methods; and a set of 13 single-drug CNNs (SD-CNN) with AUCs 80.1-97.1% and higher specificity than the previous state-of-the-art. Using saliency methods to evaluate the contribution of input sequence features to the SD-CNN predictions, we identify 18 sites in the genome not previously associated with resistance. The CNN models permit functional variant discovery, biologically meaningful interpretation, and clinical applicability. Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution to multi-drug resistance diagnosis. Here, the authors present two deep convolutional neural networks that predict the antibiotic resistance phenotypes of M. tuberculosis isolates.
- Subjects
CONVOLUTIONAL neural networks; MYCOBACTERIUM tuberculosis; DRUG resistance in microorganisms; ARTIFICIAL neural networks; WHOLE genome sequencing; MULTIDRUG resistance; PHENOTYPES
- Publication
Nature Communications, 2022, Vol 13, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-022-31236-0