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通过监督机器学习预测抗微生物和抗微生物剂的协同效应

Basak Olcay¹, Gizem D Ozdemir², Mehmet A Ozdemir³

¹Department of Biomedical Engineering, Graduate School of Natural and Applied Sciences, Izmir Katip Celebi University, Izmir, Turkey.

BMC biomedical engineering

|January 17, 2024

概括

机器学习模型可以预测抗微生物 (AMP) 和药剂之间的协同效应,从而降低实验成本. 这种方法有助于更有效地开发新的抗微生物疗法.

科学领域:

生物化学生物化学
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:

传染病对健康和医疗保健造成重大负担,需要有效的治疗.
传统的抗微生物剂和新型抗微生物 (AMP) 被使用,但局限性仍然存在.
结合AMP和抗菌剂显示出有希望的结果,但协同作用的确定是实验密集的.

研究的目的:

开发和评估机器学习 (ML) 模型,用于预测AMP和抗菌剂的协同效应.
解决实验协同作用测试的耗时和资源密集性.
探索ML在加速抗微生物药物发现方面的潜力.

主要方法:

实施几个监督的ML模型来预测抗微生物协同作用.
ML模型的超参数优化,特别关注光梯度增强机器分类器 (oLGBMC).
功能重要性分析,以确定协同效应的关键预测因素.

主要成果:

优化的光梯度增强机器分类器 (oLGBMC) 在预测协同效应方面取得了76.92%的测试准确率.
关键的预测特征包括目标微生物物种,药物的最小抑制度 (MIC) 和使用的特定抗菌剂.
结果与现有的实验发现保持一致,验证了ML模型的预测.

结论:

机器学习算法可以准确预测抗菌组合的协同活性,绕过复杂的实验分析.
这种基于机器学习的方法可以显著降低实验成本和时间.
机器学习模型为验证实验结果和指导未来抗菌药物开发研究提供了宝贵的工具.

关键词:

抗微生物药物是一种抗菌剂.抗微生物是一种抗微生物.人工智能的人工智能是人工智能.分数抑制度的分数抑制度.机器学习是机器学习.有协同效应的效应.

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