生物步行-MDA:一种基于多层生物医学知识图的新方法,用于大规模预测代谢物-药物关联
Xiaoliang Wu1, Meitao Wu1, Yetong Yang1
1College of Bioinformatics Science and Technology, Harbin Medical University, No. 157, Baojian Road, Nangang District, Harbin, Heilongjiang 150081, China.
生物步行-MDA使用多层知识图来预测新型代谢物-药物相互作用. 这种计算框架增强了对药物代谢的理解,并有助于开发新的治疗策略.
科学领域:
- 生物医学信息学 生物医学信息学
- 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:
- 代谢物水平对疾病进展和人类健康至关重要.
- 药物代谢物相互作用是复杂的,影响药物的疗效,毒性和相互作用.
- 关于代谢物与药物相关性的现有数据往往不完整和杂.
研究的目的:
- 开发一个计算框架,BioWalk-MDA,用于大规模预测新型代谢物-药物相互作用.
- 将各种生物数据集成到多层生物医学知识图 (Multi-BiomedKGs) 中.
- 为了提高对复杂的代谢物 - 药物关系的理解.
主要方法:
- 构建多种生物医学基因组,整合蛋白质,微生物和疾病数据.
- 使用随机步行和异质的Skip-gram模型进行特征提取.
- 使用完全连接的神经网络 (FCNN) 来推断新的关联.
主要成果:
- 生物步行-MDA实现了高预测性能,平均准确率为0.971,0.995 AUROC和0.994 AUPRC.
- 与现有方法相比,该框架在5倍交叉验证中表现出更高的性能.
- 案例研究验证了BioWalk-MDA在预测血液代谢物和心血管药物的相互作用方面的可靠性和效率.
结论:
- 生物步行-MDA是一种强大的计算工具,用于预测新型代谢物-药物相互作用.
- 该框架有助于探索复杂的生物关系.
- 预计BioWalk-MDA将有助于药物开发和组合疗法的设计.
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