TY  - EJOU
AU  - Tran, Dang Hung 
AU  - Nguyen, Van Tinh 

TI  - CFGANLDA: A Collaborative Filtering and Graph Attention Network-Based Method for Predicting Associations between lncRNAs and Diseases
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 3
SN  - 1546-2226

AB  - 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.
KW  - LncRNA-disease associations; collaborative filtering; principal component analysis; graph attention network; deep learning

DO  - 10.32604/cmc.2025.063228