Prediction of systemic lupus erythematosus-related genes based on graph attention network and deep neural network

被引：0
作者：
Fang F. [1 ]
Sun Y. [2 ]
机构：
[1] Department of Rheumatology and Immunology, The First Hospital of China Medical University, Liaoning, Shenyang
[2] Department of Ophthalmology, The First Hospital of China Medical University, Liaoning, Shenyang
来源：
Computers in Biology and Medicine | 2024年 / 175卷
关键词：
Deep neural network; Gene; Graph attention network; Systemic lupus erythematosus;
D O I：
10.1016/j.compbiomed.2024.108371
中图分类号：
学科分类号：
摘要：
Systemic lupus erythematosus (SLE) is an autoimmune disorder intricately linked to genetic factors, with numerous approaches having identified genes linked to its development, diagnosis and prognosis. Despite genome-wide association analysis and gene knockout experiments confirming some genes associated with SLE, there are still numerous potential genes yet to be discovered. The search for relevant genes through biological experiments entails significant financial and human resources. With the advancement of computational technologies like deep learning, we aim to identify SLE-related genes through deep learning methods, thereby narrowing down the scope for biological experimentation. This study introduces SLEDL, a deep learning-based approach that leverages DNN and graph neural networks to effectively identify SLE-related genes by capturing relevant features in the gene interaction network. The above steps transform the identification of SLE related genes into a binary classification problem, ultimately solved through a fully connected layer. The results demonstrate the superiority of SLEDL, achieving higher AUC (0.7274) and AUPR (0.7599), further validated through case studies. © 2024 Elsevier Ltd
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