一个新的综合框架,用于识别潜在的病毒与毒品的关联
Jia Qu1, Zihao Song1, Xiaolong Cheng1
1School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China.
Frontiers in microbiology
|September 7, 2023
概括
这项研究整合了计算模型来预测抗病毒药物,提供了一种具有成本效益的方法来对抗抗药性. 开发的模型有效地识别了潜在的病毒-药物关联和微生物的新药候选者.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 抗病毒药物耐药性是一个日益严重的全球健康问题.
- 药物再利用为识别新型治疗剂提供了一个可行的策略.
- 计算模型可以加快对可重复使用药物候选物的预测.
研究的目的:
- 开发和评估用于预测抗病毒药物的综合计算模型.
- 利用矩阵分解方法来识别潜在的药物病毒关联.
- 用各种交叉验证技术来评估模型的性能.
主要方法:
- 矩阵分解与异质图推理 (MDHGI) 和局限核规范规范化 (BNNR) 的整合.
- 全球一次性交叉验证 (LOOCV),本地LOOCV和5倍交叉验证的应用.
- 使用DrugVirus,MDAD和aBiofilm数据集进行验证,其中包括已知的药物病毒和药物微生物关联.
主要成果:
- 实现了高性能指标,AUC值为0.9035 (全球LOOCV) 和0.8786 (本地LOOCV).
- 在5倍的交叉验证中,DrugVirus数据集的平均AUC为0.8856 ± 0.0032.
- 案例研究证实了该模型在识别潜在的病毒药物关联和预测新型微生物药物的有效性.
结论:
- 综合计算模型表明,在抗病毒疗法中药物重用具有显著的潜力.
- 该模型在不同数据集中的强大性能突出显示了其可概括性.
- 这种方法为发现抗病毒感染和微生物疾病的新疗法提供了一种有效的方法.
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