LncRNA-disease association prediction method based on heterogeneous information completion and convolutional neural network
<|reference_start|>LncRNA-disease association prediction method based on heterogeneous information completion and convolutional neural network: The emerging research shows that lncRNA has crucial research value in a series of complex human diseases. Therefore, the accurate identification of lncRNA-disease associations (LDAs) is very important for the warning and treatment of diseases. However, most of the existing methods have limitations in identifying nonlinear LDAs, and it remains a huge challenge to predict new LDAs. In this paper, a deep learning model based on a heterogeneous network and convolutional neural network (CNN) is proposed for lncRNA-disease association prediction, named HCNNLDA. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair is constructed according to various biological premises about lncRNAs, diseases, and miRNAs. Then, the low-dimensional feature representation is fully learned by the convolutional neural network. In the end, the XGBoot classifier model is trained to predict the potential LDAs. HCNNLDA obtains a high AUC value of 0.9752 and AUPR of 0.9740 under the 5-fold cross-validation. The experimental results show that the proposed model has better performance than that of several latest prediction models. Meanwhile, the effectiveness of HCNNLDA in identifying novel LDAs is further demonstrated by case studies of three diseases. To sum up, HCNNLDA is a feasible calculation model to predict LDAs.<|reference_end|>
@article{xi2024lncrna-disease,
    title={LncRNA-disease association prediction method based on heterogeneous
  information completion and convolutional neural network},
    author={Wen-Yu Xi, Juan Wang, Yu-Lin Zhang, Jin-Xing Liu, Yin-Lian Gao},
    journal={arXiv preprint arXiv:2406.03406},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03406},
    primaryClass={cs.LG cs.AI q-bio.QM}
}