Multi-Label Random Forest Model for Tuberculosis Drug Resistance Classification and Mutation Ranking.

ABSTRACT: Resistance prediction and mutation ranking are important tasks in the analysis of Tuberculosis sequence data. Due to standard regimens for the use of first-line antibiotics, resistance co-occurrence, in which samples are resistant to multiple drugs, is common. Analysing all drugs simultaneously should therefore enable patterns reflecting resistance co-occurrence to be exploited for resistance prediction. Here, multi-label random forest (MLRF) models are compared with single-label random forest (SLRF) for both predicting phenotypic resistance from whole genome sequences and identifying important mutations for better prediction of four first-line drugs in a dataset of 13402 Mycobacterium tuberculosis isolates. Results confirmed that MLRFs can improve performance compared to convention

SUBMITTER: Kouchaki S 

PROVIDER: S-EPMC7188832 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature