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An interpretable classification method for predicting drug resistance in M. tuberculosis

Hooman Zabeti, Nick Dexter, Amir Hosein Safari, Nafiseh Sedaghat, View ORCID ProfileMaxwell Libbrecht, View ORCID ProfileLeonid Chindelevitch
doi: https://doi.org/10.1101/2020.05.31.115741
Hooman Zabeti
1School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
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  • For correspondence: hzabeti@sfu.ca
Nick Dexter
2Department of Mathematics, Simon Fraser University, Burnaby, BC, V5A1S6, Canada
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Amir Hosein Safari
1School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
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Nafiseh Sedaghat
1School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
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Maxwell Libbrecht
1School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
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  • ORCID record for Maxwell Libbrecht
Leonid Chindelevitch
1School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
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  • ORCID record for Leonid Chindelevitch
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Abstract

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_RuleBasedClassifier

Contact hooman_zabeti{at}sfu.ca

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • 2012 ACM Subject Classification Applied computing – Life and medical sciences – Computational biology – Molecular sequence analysis

  • Figure 4 revised;Section 4 updated to clarify the result

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 13, 2020.
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An interpretable classification method for predicting drug resistance in M. tuberculosis
Hooman Zabeti, Nick Dexter, Amir Hosein Safari, Nafiseh Sedaghat, Maxwell Libbrecht, Leonid Chindelevitch
bioRxiv 2020.05.31.115741; doi: https://doi.org/10.1101/2020.05.31.115741
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An interpretable classification method for predicting drug resistance in M. tuberculosis
Hooman Zabeti, Nick Dexter, Amir Hosein Safari, Nafiseh Sedaghat, Maxwell Libbrecht, Leonid Chindelevitch
bioRxiv 2020.05.31.115741; doi: https://doi.org/10.1101/2020.05.31.115741

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