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LncRNA-disease association prediction method based on heterogeneous information completion and convolutional neural network by Xi, Wen-Yu; Wang, Juan; Zhang, Yu-Lin; Liu, Jin-Xing; Gao, Yin-Lian is a scholarly article available to read on EtoBox.

What is LncRNA-disease association prediction method based on heterogeneous information completion and convolutional neural network about?

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 predicti

Author

Xi, Wen-Yu; Wang, Juan; Zhang, Yu-Lin; Liu, Jin-Xing; Gao, Yin-Lian

Published

2024

Language

EN