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一个基于微生物知识图的深度学习模型,用于预测目标宿主候选微生物

Jie Pan¹, Zhen Zhang¹, Ying Li¹

¹Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi'an 710069, China.

Briefings in bioinformatics

|March 30, 2024

概括

这项研究介绍了KGVHI,一种使用异质微生物网络和深度学习来预测微生物与宿主相互作用 (MHI) 的新型计算模型. KGVHI准确地识别了宿主潜在的微生物病原体,有助于理解微生物生态和开发向疗法.

科学领域:

微生物生态学微生物生态学
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

预测微生物与宿主相互作用 (MHI) 对于理解微生物组动态,微生物进化和全球健康至关重要.
描述复杂的微生物宿主信号机制是一个重大挑战.
计算方法为确定MHI的实验方法提供了具有成本效益的替代方案.

研究的目的:

开发一种新的计算模型,用于预测与特定宿主相互作用的候选微生物.
利用异质微生物网络 (HMN) 和知识图嵌入用于MHI预测.
提高对微生物与宿主关系的基本监管机制的理解.

主要方法:

构建了一个异构的微生物网络 (HMN),集成人类蛋白质,病毒和病原细菌及其属性.
采用知识图嵌入策略来捕捉全球网络拓.
利用自然语言处理 (NLP) 来提取本地生物属性信息.
将本地和全球信息集成到一个混合深度神经网络 (DNN) 中,用于预测.

主要成果:

在三个MHI数据集上,KGVHI模型取得了卓越的性能,超过了最先进的方法.
涉及致病细菌的案例研究表明,KGVHI对潜在的MHI对有很强的预测能力.
该模型有效地结合了网络结构和生物属性,用于准确的MHI预测.

结论:

KGVHI提供了一种强大的计算工具,用于预测微生物与宿主之间的相互作用.
这些发现有助于深入了解微生物生态和开发针对微生物感染的向疗法.
这种方法有助于系统地描述微生物与宿主之间的信号传递.

关键词:

生物信息学是一种生物信息学.深度学习是一种深度学习.不同质的微生物网络 (HMN)知识图 (KG) 是一个知识图.微生物与宿主相互作用 (MHI)

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