GCFMCL:使用图形协作过和多视图对比学习预测miRNA药物敏感性

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Jinhang Wei¹, Linlin Zhuo², Zhecheng Zhou¹

¹College of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325027 Wenzhou, China.

Briefings in bioinformatics

|July 10, 2023

概括

这项研究介绍了GCFMCL,这是一种用于预测miRNA药物敏感性的新型深度学习模型. 通过在图形数据上使用多视图对比学习,GCFMCL提高了准确性,优于现有的方法.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

微RNA (miRNA) 机制对药物作用至关重要,影响药物发现和生物标志物研究.
对于miRNA药物敏感性的传统实验方法是昂贵和耗时的.
现有的深度学习方法在miRNA与药物相互作用中的稀疏数据和复杂特征信息方面扎.

研究的目的:

开发一个高效和准确的计算模型来预测miRNA药物敏感性.
解决目前深度学习方法分析miRNA药物关系的局限性.
在图形协作过框架内利用多视图对比学习.

主要方法:

提出了GCFMCL,这是一个包含多视图对比学习的图形协作过模型.
开发了使用邻里信息的拓对比学习.
实现特征对比学习来挖掘高阶特征相关性和邻近关系.

主要成果:

在2049个miRNA药物关联的数据集上,GCFMCL在AUC为95.28%,AUPR为95.66%,F1得分为89.77%的高性能.
该模型显著超过了最先进的方法.
多视图对比学习有效地缓解了稀疏数据和噪音的问题.

结论:

GCFMCL提供了一种强大的新方法来预测miRNA药物敏感性.
该模型的性能证明了多视图对比学习在这个领域的有效性.
这项工作为药物向发现和相关研究提供了宝贵的工具.

关键词:

协作过是一种协作过.毒品毒品毒品是一种药物.这是一个小RNARNA.多视图对比学习学习灵敏度灵敏度灵敏度灵敏度灵敏度

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