A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis

Anna G. Green, Chang Ho Yoon, Michael L. Chen, Yasha Ektefaie, Mack Fina, Luca Freschi, Matthias I. Gröschel, Isaac Kohane, Andrew Beam () and Maha Farhat ()
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Anna G. Green: Harvard Medical School
Chang Ho Yoon: Harvard Medical School
Michael L. Chen: Harvard Medical School
Yasha Ektefaie: Harvard Medical School
Mack Fina: Harvard College
Luca Freschi: Harvard Medical School
Matthias I. Gröschel: Harvard Medical School
Isaac Kohane: Harvard Medical School
Andrew Beam: Harvard Medical School
Maha Farhat: Harvard Medical School

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-31236-0

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