利用拓扑正则化深度学习多模态网络,实现药物重新定位

IF 7.1 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Yuto Ohnuki, Manato Akiyama, Yasubumi Sakakibara
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引用次数: 0

摘要

动机用于药物-疾病预测的计算技术对于促进药物发现和重新定位至关重要。虽然许多方法都利用了来自各种生物数据库的多模态网络,但很少有方法能整合全面的多组学数据,包括转录组、蛋白质组和代谢组。我们介绍的 STRGNN 是一种新颖的图深度学习方法,它利用由蛋白质、RNA、代谢物和化合物组成的广泛多模态网络预测药物与疾病的关系。我们构建了一个包含多组学数据的详细数据集,并开发了一种具有拓扑正则化的学习算法。结果与现有方法相比,STRGNN 的准确性更胜一筹,并发现了几种新的药物效应,证实了现有文献的观点。STRGNN 成为药物预测和发现的强大工具。STRGNN 的源代码以及用于性能评估的数据集可在 https://github.com/yuto-ohnuki/STRGNN.git 网站上获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning of multimodal networks with topological regularization for drug repositioning

Motivation

Computational techniques for drug-disease prediction are essential in enhancing drug discovery and repositioning. While many methods utilize multimodal networks from various biological databases, few integrate comprehensive multi-omics data, including transcriptomes, proteomes, and metabolomes. We introduce STRGNN, a novel graph deep learning approach that predicts drug-disease relationships using extensive multimodal networks comprising proteins, RNAs, metabolites, and compounds. We have constructed a detailed dataset incorporating multi-omics data and developed a learning algorithm with topological regularization. This algorithm selectively leverages informative modalities while filtering out redundancies.

Results

STRGNN demonstrates superior accuracy compared to existing methods and has identified several novel drug effects, corroborating existing literature. STRGNN emerges as a powerful tool for drug prediction and discovery. The source code for STRGNN, along with the dataset for performance evaluation, is available at https://github.com/yuto-ohnuki/STRGNN.git.

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来源期刊
Journal of Cheminformatics
Journal of Cheminformatics CHEMISTRY, MULTIDISCIPLINARY-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
14.10
自引率
7.00%
发文量
82
审稿时长
3 months
期刊介绍: Journal of Cheminformatics is an open access journal publishing original peer-reviewed research in all aspects of cheminformatics and molecular modelling. Coverage includes, but is not limited to: chemical information systems, software and databases, and molecular modelling, chemical structure representations and their use in structure, substructure, and similarity searching of chemical substance and chemical reaction databases, computer and molecular graphics, computer-aided molecular design, expert systems, QSAR, and data mining techniques.
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