Gene-related multi-network collaborative deep feature learning for predicting miRNA-disease associations

IF 4 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Computers & Electrical Engineering Pub Date : 2025-03-14 DOI:10.1016/j.compeleceng.2025.110242
Pengli Lu, Xu Cao
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引用次数: 0

Abstract

A growing number of experiments have shown that microRNAs (miRNAs) play a key role in regulating gene expression, and their aberrant expression may lead to the development of specific diseases. Therefore, accurate identification of the associations between miRNAs and diseases is crucial for the prevention, diagnosis and treatment of miRNA-related diseases. However, existing models have limitations in accurately capturing biological information and comprehensively extracting features. To address this problem, we propose gene-related multi-network collaborative deep feature learning for predicting miRNA-disease associations (MNFLMDA). First, we constructed three heterogeneous networks, miRNA-gene, disease-gene and miRNA-disease, and mined the potential information of the heterogeneous networks using Auto-Encoder and Graph Attention Networks. Subsequently, this potential information was fused to form the final features. Finally, these features were used to predict the associations between miRNAs and diseases. To validate the effectiveness of the model, we conducted extensive experiments on the Human miRNA Disease Database and compared it with eight of the most representative models over the past two years, and the results showed that MNFLMDA exhibits excellent performance. In addition, case studies of breast tumors, colorectal tumors and hepatocellular carcinoma were conducted to further validate the predictive performance of MNFLMDA.
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来源期刊
Computers & Electrical Engineering
Computers & Electrical Engineering 工程技术-工程:电子与电气
CiteScore
9.20
自引率
7.00%
发文量
661
审稿时长
47 days
期刊介绍: The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency. Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.
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