1. A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis.
- Author
-
Green AG, Yoon CH, Chen ML, Ektefaie Y, Fina M, Freschi L, Gröschel MI, Kohane I, Beam A, and Farhat M
- Subjects
- Anti-Bacterial Agents, Drug Resistance, Bacterial genetics, Humans, Mutation, Neural Networks, Computer, Mycobacterium tuberculosis genetics, Tuberculosis drug therapy, Tuberculosis genetics
- 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., (© 2022. The Author(s).)
- Published
- 2022
- Full Text
- View/download PDF