INGOT-DR: an interpretable classifier for predicting drug resistance in M. tuberculosis

INGOT-DR: an interpretable classifier for predicting drug resistance in M. tuberculosis

Abstract Motivation Prediction of drug resistance and identification of its mechanisms in bacteria such as Mycobacterium tuberculosis, the etiological agent of tuberculosis, is a challenging problem. Solving this problem requires a transparent, accurate, and flexible predictive model. The methods cu...

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Bibliographic Details
Main Authors: Hooman Zabeti, Nick Dexter, Amir Hosein Safari, Nafiseh Sedaghat, Maxwell Libbrecht, Leonid Chindelevitch
Format: Article
Language:English
Published: BMC 2021-08-01
Series:Algorithms for Molecular Biology
Subjects:
Drug resistance
Interpretable machine learning
Group testing
Integer linear programming
Rule-based learning
Whole-genome sequencing
Online Access:https://doi.org/10.1186/s13015-021-00198-1

Internet

https://doi.org/10.1186/s13015-021-00198-1

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