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DeepWalk-aware graph attention networks with CNN for circRNA–drug sensitivity association identification

计算机科学 图形 图嵌入 药物重新定位 人工智能 注意力网络 特征(语言学) 机器学习 特征学习 灵敏度(控制系统) 嵌入 计算生物学 药品 理论计算机科学 生物 工程类 药理学 哲学 语言学 电子工程
作者
Guanghui Li,Youjun Li,Cheng Liang,Jiawei Luo
出处
期刊:Briefings in Functional Genomics [Oxford University Press]
被引量:2
标识
DOI:10.1093/bfgp/elad053
摘要

Abstract Circular RNAs (circRNAs) are a class of noncoding RNA molecules that are widely found in cells. Recent studies have revealed the significant role played by circRNAs in human health and disease treatment. Several restrictions are encountered because forecasting prospective circRNAs and medication sensitivity connections through biological research is not only time-consuming and expensive but also incredibly ineffective. Consequently, the development of a novel computational method that enhances both the efficiency and accuracy of predicting the associations between circRNAs and drug sensitivities is urgently needed. Here, we present DGATCCDA, a computational method based on deep learning, for circRNA–drug sensitivity association identification. In DGATCCDA, we first construct multimodal networks from the original feature information of circRNAs and drugs. After that, we adopt DeepWalk-aware graph attention networks to sufficiently extract feature information from the multimodal networks to obtain the embedding representation of nodes. Specifically, we combine DeepWalk and graph attention network to form DeepWalk-aware graph attention networks, which can effectively capture the global and local information of graph structures. The features extracted from the multimodal networks are fused by layer attention, and eventually, the inner product approach is used to construct the association matrix of circRNAs and drugs for prediction. The ultimate experimental results obtained under 5-fold cross-validation settings show that the average area under the receiver operating characteristic curve value of DGATCCDA reaches 91.18%, which is better than those of the five current state-of-the-art calculation methods. We further guide a case study, and the excellent obtained results also show that DGATCCDA is an effective computational method for exploring latent circRNA–drug sensitivity associations.
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