转移学习预测新兴病原体中的物种特异性药物相互作用
Carolina H Chung1, David C Chang1, Nicole M Rhoads1,2
1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
bioRxiv : the preprint server for biology
|June 19, 2024
概括
一个新的框架,TACTIC,使用转移学习来预测有效的药物组合细菌有限的数据. 这种方法确定了协同作用的药物组合,以对抗具有挑战性的病原体中的抗生素耐药性.
科学领域:
- 微生物学 微生物学
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 机器学习 (ML) 对于识别抗药性细菌的药物组合至关重要.
- 现有的ML模型在缺乏足够的训练数据的情况下与病原体作斗争.
研究的目的:
- 开发一种新的框架 (TACTIC) 用于利用转移学习和众包来预测未经研究的细菌中的药物相互作用.
- 识别新型协同作用药物组合,有效对抗格兰氏阴性和非结核性菌根 (NTM) 病原体.
主要方法:
- 开发了TACTIC框架,将转移学习和众包集成到12种细菌菌株的2,965种药物相互作用中.
- 应用TACTIC来预测~600,000种不同细菌物种和代谢环境中的药物相互作用.
- 经过实验验证的预测协同效应组合对抗M..
主要成果:
- 对于具有有限数据的物种,TACTIC在预测药物相互作用方面表现优于传统的ML模型.
- 鉴定出选择性协同作用的药物组合,对抗像*A. baumannii*和NTM.这样的格拉姆阴性病原体.
- 经过验证的协同作用组合,包括清素,安皮西林和梅西林,用于 *M. abscessus*.
结论:
- TACTIC 能够有效地预测新兴病原体的药物组合,但数据稀少.
- 确定了有前途的药物组合来对抗格兰氏阴性和NTM感染中的抗生素耐药性.
- 为治疗细菌性眼部感染 (内炎) 提出了新的协同作用组合.
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