MHIPM:使用异构微生物网络的多视图特征准确预测微生物与宿主相互作用
Jie Pan1, Guangming Zhang1, Yong Yang1
1Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China.
Journal of chemical information and modeling
|September 18, 2024
概括
我们开发了MHIPM,这是一种深度学习方法,用于预测微生物与宿主相互作用 (MHI). 这种计算方法有效地识别了潜在的微生物与宿主关系,有助于进一步的生物验证和对微生物群的理解.
科学领域:
- 微生物学 微生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 微生物与宿主相互作用 (MHI) 对人类健康至关重要.
- 识别MHI的湿实验室方法是缓慢而昂贵的.
- 需要计算方法来优先考虑候选微生物与宿主相互作用.
研究的目的:
- 开发一种新的深度学习方法,MHIPM,用于预测MHI.
- 为了利用多源生物信息进行MHI预测.
- 为识别潜在的微生物与宿主关系提供一个高效的计算工具.
主要方法:
- 构建了一个异构的微生物网络,包括人类蛋白质,病毒,菌体和细菌.
- 采用ESM-2和doc2vec,以自我关注从蛋白质序列中提取特征.
- 使用 GraphSAGE 来捕获异质网络中的网络特征.
主要成果:
- 与七个基线算法和四个变体相比,MHIPM在三个预测任务中表现出卓越的性能.
- 案例研究和分子对接实验验证实了该模型的有效性.
- 该模型成功地确定了可信的生物实验候选微生物.
结论:
- MHIPM是一种高效和强大的方法,用于预测微生物与宿主相互作用.
- 该工具有助于优先考虑候选微生物进行实验验证.
- MHIPM有助于更好地了解微生物组在人类健康中的作用.
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