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FLONE:完全嵌入洛伦茨网络,用于推断新药标

Yang Yue¹, David McDonald², Luoying Hao¹

¹Centre for Computational Biology, School of Computer Science, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.

Bioinformatics advances

|June 5, 2023

概括

我们介绍FLONE,一种用于药物标预测的新型超标嵌入方法. 通过利用层次性的药物-疾病-点网络和考虑疾病类型,FLONE准确地识别药物点,优于现有的欧几里德方法.

科学领域:

计算生物学是一种计算生物学.
机器学习机器学习
生物信息学是一种生物信息学.

背景情况:

基于图形的机器学习被广泛用于药物-疾病-标 (DDT) 网络中的药物标预测.
现有的方法在预测过程中经常无法考虑疾病类型,并且无法充分利用DDT网络中的等级信息.
这种限制可能导致药物标预测准确度低于最佳.

研究的目的:

通过明确考虑疾病类型和利用DDT网络的等级结构来改善药物标预测.
提出一种基于超标嵌入的新方法,FLONE,用于增强目标预测.
通过结合外部领域的知识,使FLONE能够处理以前未见的药物和目标.

主要方法:

制定药物标预测作为知识图表完成任务.
开发了FLONE,这是一种超标嵌入方法,旨在捕获DDT网络中的层次拓信息.
设计了超标编码器,以整合外部领域的知识来处理新型样品.

主要成果:

与欧几里德嵌入方法相比,FLONE在两个DDT网络上在药物目标预测方面表现出卓越的准确性.
超标空间的使用有效地捕捉了DDT网络中的等级关系.
超标编码器使FLONE能够对未见的药物和目标进行概括,提高了其实际适用性.

结论:

超标嵌入是一种强大的方法,用于建模层次的DDT网络,并改进药物目标预测.
FLONE为识别药物点提供了更准确和多功能解决方案,特别是在考虑特定疾病背景时.
该方法处理新型实体的能力使其在现实世界药物发现场景中具有价值.

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