RT Journal Article SR Electronic T1 An interpretable classification method for predicting drug resistance in M. tuberculosis JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.05.31.115741 DO 10.1101/2020.05.31.115741 A1 Hooman Zabeti A1 Nick Dexter A1 Amir Hosein Safari A1 Nafiseh Sedaghat A1 Maxwell Libbrecht A1 Leonid Chindelevitch YR 2020 UL http://biorxiv.org/content/early/2020/07/13/2020.05.31.115741.abstract AB Motivation The prediction of drug resistance and the identification of its mechanisms in bacteria such as Mycobacterium tuberculosis, the etiological agent of tuberculosis, is a challenging problem. Modern methods based on testing against a catalogue of previously identified mutations often yield poor predictive performance. On the other hand, machine learning techniques have demonstrated high predictive accuracy, but many of them lack interpretability to aid in identifying specific mutations which lead to resistance. We propose a novel technique, inspired by the group testing problem and Boolean compressed sensing, which yields highly accurate predictions and interpretable results at the same time.Results We develop a modified version of the Boolean compressed sensing problem for identifying drug resistance, and implement its formulation as an integer linear program. This allows us to characterize the predictive accuracy of the technique and select an appropriate metric to optimize. A simple adaptation of the problem also allows us to quantify the sensitivity-specificity trade-off of our model under different regimes. We test the predictive accuracy of our approach on a variety of commonly used antibiotics in treating tuberculosis and find that it has accuracy comparable to that of standard machine learning models and points to several genes with previously identified association to drug resistance.Availability https://github.com/hoomanzabeti/TB_Resistance_RuleBasedClassifierContact hooman_zabeti{at}sfu.caCompeting Interest StatementThe authors have declared no competing interest.