Machine learning-based approach for identification of new resistance associated mutations from whole genome sequences of <i>Mycobacterium tuberculosis</i>.

Bioinform Adv · 2025 · PMC11930343 · PMID 40125545

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highly significantno p-value reported

We also checked the abundance and SHAP score of highly significant ML-predicted mutations in both resistant and susceptible populations and compared them with the abundance of WHO mutations which overlapped with ML predicted mutations.

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Machine learning-based approach for identification of new resistance associated mutations from whole genome sequences of &lt;i&gt;Mycobacterium tuberculosis&lt;/i&gt;.

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Machine learning-based approach for identification of new resistance associated mutations from whole genome sequences of <i>Mycobacterium tuberculosis</i>.