Pal Ankita, Mohanty Debasisa
Bioinformatics Center, National Institute of Immunology, New Delhi 110067, India.
Bioinform Adv. 2025 Mar 11;5(1):vbaf050. doi: 10.1093/bioadv/vbaf050. eCollection 2025.
Currently available methods for the prediction of genotypic drug resistance in utilize information on known markers of drug resistance. Hence, machine learning approaches are needed that can discover new resistance markers.
Whole genome sequences with known phenotypic drug resistance profiles have been utilized to train XGBoost and ANN classifiers for 5 first-line and 8 second-line tuberculosis drugs. Benchmarking on a completely independent dataset from CRyPTIC database revealed that our method has high sensitivity (90%-95%) and specificity (94%-99%) for five first-line drugs and robust performance for six second-line drugs with a sensitivity of 77%-89% at over 95% specificity. An explainable AI method, SHapley Additive exPlanations, has successfully identified resistance mutations for each drug in a completely automated way. This approach could not only identify known resistance associated mutations in agreement with the WHO mutation catalogue, but also predicted >100 other potential resistance associated mutations for 13 antibiotics in new genes outside the known resistance loci. Identification of new resistance markers opens up the opportunity for the discovery of novel mechanisms of drug resistance.
Our prediction method has been implemented as TB-AMRpred webserver and command line tool, available freely at http://www.nii.ac.in/TB-AMRpred.html and https://github.com/Ankitapal1995/TB-AMRprd.
目前用于预测基因型耐药性的方法利用了已知耐药性标志物的信息。因此，需要能够发现新耐药性标志物的机器学习方法。
已利用具有已知表型耐药性谱的全基因组序列来训练针对5种一线和8种二线结核病药物的XGBoost和人工神经网络分类器。在来自CRYPTIC数据库的完全独立数据集上进行的基准测试表明，我们的方法对五种一线药物具有高灵敏度（90%-95%）和特异性（94%-99%），对六种二线药物具有稳健的性能，在特异性超过95%时灵敏度为77%-89%。一种可解释的人工智能方法，即SHapley加性解释，已成功以完全自动化的方式识别了每种药物的耐药性突变。这种方法不仅可以识别与世界卫生组织突变目录一致的已知耐药相关突变，还预测了已知耐药位点之外新基因中13种抗生素的100多个其他潜在耐药相关突变。新耐药性标志物的识别为发现新的耐药机制提供了机会。
我们的预测方法已实现为TB-AMRpred网络服务器和命令行工具，可在http://www.nii.ac.in/TB-AMRpred.html和https://github.com/Ankitapal1995/TB-AMRprd免费获得。
