Open Access

ARTICLE

CFGANLDA: A Collaborative Filtering and Graph Attention Network-Based Method for Predicting Associations between lncRNAs and Diseases

Dang Hung Tran, Van Tinh Nguyen^*

School of Information and Communications Technology, Hanoi University of Industry, No. 298 Cau Dien Street, Bac Tu Liem District, Hanoi, 100000, Vietnam

* Corresponding Author: Van Tinh Nguyen. Email:

(This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)

Computers, Materials & Continua 2025, 83(3), 4679-4698. https://doi.org/10.32604/cmc.2025.063228

Received 09 January 2025; Accepted 02 April 2025; Issue published 19 May 2025

Abstract

It is known that long non-coding RNAs (lncRNAs) play vital roles in biological processes and contribute to the progression, development, and treatment of various diseases. Obviously, understanding associations between diseases and lncRNAs significantly enhances our ability to interpret disease mechanisms. Nevertheless, the process of determining lncRNA-disease associations is costly, labor-intensive, and time-consuming. Hence, it is expected to foster computational strategies to uncover lncRNA-disease relationships for further verification to save time and resources. In this study, a collaborative filtering and graph attention network-based LncRNA-Disease Association (CFGANLDA) method was nominated to expose potential lncRNA-disease associations. First, it takes into account the advantages of using biological information from multiple sources. Next, it uses a collaborative filtering technique in order to address the sparse data problem. It also employs a graph attention network to reinforce both linear and non-linear features of the associations to advance prediction performance. The computational results indicate that CFGANLDA gains better prediction performance compared to other state-of-the-art approaches. The CFGANLDA’s area under the receiver operating characteristic curve (AUC) metric is 0.9835, whereas its area under the precision-recall curve (AUPR) metric is 0.9822. Statistical analysis using 10-fold cross-validation experiments proves that these metrics are significant. Furthermore, three case studies on prostate, liver, and stomach cancers attest to the validity of CFGANLDA performance. As a result, CFGANLDA method proves to be a valued tool for lncRNA-disease association prediction.

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