基于多信息集成的药物靶标预测方法研究

doi:10.16088/j.issn.1001-6600.2021072301

Abstract

Abstract: Accurate determination of drug-target interactions is crucial in drug discovery process and repositioning. Traditional methods for DTI prediction are either time-consuming (simulation-based methods) or heavily dependent on domain expertise (similarity-based and feature-based methods). Existing computation-based methods using single data information or sparse data, always suffer from high false positive rates. Although integrating multiple heterogeneous networks has been prevalent for drug target prediction, how to retain as much structural information as possible is still a big challenge. This paper proposes a novel framework NGDTI, which extracts relevant biological properties and association information from the network while maintaining the topology information. Further, the graph neural network is applied to update the extracted feature information. The learned topology-preserving representations of drugs and targets promote DTI prediction. Compared with the state-of-the-art methods, NGDTI increases the AUPR value by nearly 0.01. The results demonstrate that NGDTI is promising for drug development and repositioning.

Key words: drug target association prediction, network embedding, network integration, matrix decomposition, graph neural network

CLC Number:

TP183

TAN Kai, LI Yongjie, PAN Haiming, HUANG Kexin, QIU Jie, CHEN Qingfeng. Study on Multi-information Integration for Drug Target Prediction[J].Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(2): 91-102.

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Study on Multi-information Integration for Drug Target Prediction

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