CHL-DTI:用于有效药物-靶点相互作用预测的新型高低阶信息收敛框架。

IF 3.9 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Shudong Wang, Yingye Liu, Yuanyuan Zhang, Kuijie Zhang, Xuanmo Song, Yu Zhang, Shanchen Pang
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引用次数: 0

摘要

认识药物与靶点的相互作用(DTI)是药物发现这一广阔领域的关键因素。传统的生物湿法实验虽然很有价值,但耗时长、成本高。近年来,以网络学习为基础的计算方法通过有效的拓扑特征提取展现出了巨大的优势,引起了广泛的研究关注。然而,现有的基于网络的学习方法大多只考虑单个药物和靶点之间的低阶二元相关性,而忽略了从多种药物和靶点中获得的潜在高阶相关信息。高阶信息作为重要组成部分,与低阶信息具有互补性。因此,将药物与靶点之间的高阶关联信息与现有的低阶信息充分整合,有可能在预测药物与靶点相互作用方面取得重大突破。我们提出了一种新颖的基于双通道网络的学习模型 CHL-DTI,它将超图中的高阶信息和普通图中的低阶信息融合在一起,用于药物-靶点相互作用预测。CHL-DTI 的高低阶信息收敛主要体现在两个方面。首先,在特征提取阶段,该模型通过结合超图和普通图,整合了高层语义信息和低层拓扑信息。其次,CHL-DTI 将创新性引入的药物-蛋白配对(DPP)超图网络结构与普通拓扑网络结构信息充分融合。在三个公开数据集上进行的大量实验表明,与 SOTA 方法相比,CHL-DTI 在 DTI 预测任务中表现出更优越的性能。CHL-DTI 的源代码可在 https://github.com/UPCLyy/CHL-DTI 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。

CHL-DTI: A Novel High-Low Order Information Convergence Framework for Effective Drug-Target Interaction Prediction.

CHL-DTI: A Novel High-Low Order Information Convergence Framework for Effective Drug-Target Interaction Prediction.

Recognizing drug-target interactions (DTI) stands as a pivotal element in the expansive field of drug discovery. Traditional biological wet experiments, although valuable, are time-consuming and costly as methods. Recently, computational methods grounded in network learning have demonstrated great advantages by effective topological feature extraction and attracted extensive research attention. However, most existing network-based learning methods only consider the low-order binary correlation between individual drug and target, neglecting the potential higher-order correlation information derived from multiple drugs and targets. High-order information, as an essential component, exhibits complementarity with low-order information. Hence, the incorporation of higher-order associations between drugs and targets, while adequately integrating them with the existing lower-order information, could potentially yield substantial breakthroughs in predicting drug-target interactions. We propose a novel dual channels network-based learning model CHL-DTI that converges high-order information from hypergraphs and low-order information from ordinary graph for drug-target interaction prediction. The convergence of high-low order information in CHL-DTI is manifested in two key aspects. First, during the feature extraction stage, the model integrates both high-level semantic information and low-level topological information by combining hypergraphs and ordinary graph. Second, CHL-DTI fully fuse the innovative introduced drug-protein pairs (DPP) hypergraph network structure with ordinary topological network structure information. Extensive experimentation conducted on three public datasets showcases the superior performance of CHL-DTI in DTI prediction tasks when compared to SOTA methods. The source code of CHL-DTI is available at https://github.com/UPCLyy/CHL-DTI .

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来源期刊
Interdisciplinary Sciences: Computational Life Sciences
Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
8.60
自引率
4.20%
发文量
55
期刊介绍: Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.
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