An explainable artificial intelligence framework reveals mutations associated with drug resistance in Mycobacterium tuberculosis

作者：Hui Cen, Peng Zhang, Yunchao Ling, Guoping Zhao, Guoqing Zhang · 发表于：Biosafety and Health · 年份：2025 · DOI：10.1016/j.bsheal.2025.11.001 · 被引用次数：2 · 研究领域：Tuberculosis Research and Epidemiology、Computational Drug Discovery Methods、Machine Learning in Bioinformatics

• Scientific questions. • The emergence of drug resistance in Mycobacterium tuberculosis (MTB) has significantly increased the complexity of tuberculosis treatment and transmission risks, underscoring the critical need to elucidate resistance mechanisms for rapid diagnostics, optimized therapeutic strategies, and global tuberculosis burden mitigation • Evidence before this study. • While genomics-based statistical association studies have identified many resistance-associated mutations, MTB continues to develop new resistance mutations under the persistent selective pressure of anti-tuberculosis drugs, emphasizing the necessity to further refine existing catalogues of resistance mutations. Although deep learning models have been applied to explain resistance-associated mutations at the population level, individual-level resistance mechanisms remain underexplored. • New findings. • We propose an explainable artificial intelligence framework called xAI-MTBDR. It combines multiple machine learning models with the SHAP method, aiming to identify new drug resistance-associated mutations and predict the drug resistance of MTB. To our knowledge, this is the largest study utilizing the MTB dataset (39,145 isolates) to assess the performance of similar models for drug resistance prediction. Compared to existing methods, xAI-MTBDR not only significantly improves the prediction accuracy of drug resistance but also provides detailed explanations of each mutation’s contribution to resista...