Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 165, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2023.107331
Keywords
lncRNA; Deep learning; Uterine fibroid; Heterogeneous network; Graph attention networks
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Long non-coding RNAs (lncRNAs) have crucial regulatory roles in cellular processes. Computational methods have emerged as valuable tools for predicting lncRNA functions and their associations with diseases. This study developed an improved computational method, RGLD, for predicting lncRNA-disease associations.
Long non-coding RNAs (lncRNAs) play crucial regulatory roles in various cellular processes, including gene expression, chromatin remodeling, and protein localization. Dysregulation of lncRNAs has been linked to several diseases, making it essential to understand their functions in disease mechanisms and therapeutic strategies. However, traditional experimental methods for studying lncRNA function are time-consuming, expensive, and offer limited insights.In recent years, computational methods have emerged as valuable tools for predicting lncRNA functions and their associations with diseases. However, many existing methods focus on constructing separate networks for lncRNA and disease similarity, resulting in information loss and insufficient processing capacity for isolated nodes. To address this, we developed 'RGLD' by combining Random Walk with restarting (RWR), Graph Neural Network (GNN), and Graph Attention Networks (GAT) to predict lncRNA-disease associations in a heterogeneous network. RGLD achieved an impressive AUC of 0.88, outperforming other methods. It can also predict novel associations between lncRNAs and diseases.RGLD identified HOTAIR, MEG3, and PVT1 as lncRNAs associated with uterine fibroids. Biological experiments directly or indirectly verified the involvement of these three lncRNAs in uterine fibroids, validating the accuracy of RGLD's predictions. Furthermore, we extensively discussed the functions of the target genes regulated by these lncRNAs in uterine fibroids, providing evidence for their role in the development and progression of the disease.
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