ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network

• Published in: BMC genomics 2023-05, Vol.24 (1), p.279-11, Article 279
• Main Authors: Meng, Xianghan, Shang, Junliang, Ge, Daohui, Yang, Yi, Zhang, Tongdui, Liu, Jin-Xing
• Format: Article
• Language: English
• Subjects: Algorithms; Alzheimer Disease - genetics; Alzheimer's disease; Analysis; Artificial neural networks; Case studies; Computer applications; DNA methylation; Embedding; Embedding transformation module; Genomics; Graph convolutional network; Head and neck carcinoma; Head and Neck Neoplasms; Health aspects; Heart failure; Heterogeneous network; Humans; Layer attention; Learning; Machine learning; Mathematical models; Medical genetics; Medical research; Medicine, Experimental; Neural networks; Neurodegenerative diseases; PiRNA-disease associations prediction; Piwi-Interacting RNA; Research Design; RNA; Semantics; Similarity; Squamous cell carcinoma; Transformations; Tumors
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-023-09380-8

Piwi-interacting RNAs (piRNAs) have been proven to be closely associated with human diseases. The identification of the potential associations between piRNA and disease is of great significance for complex diseases. Traditional "wet experiment" is time-consuming and high-priced, predicting the piRNA-disease associations by computational methods is of great significance. In this paper, a method based on the embedding transformation graph convolution network is proposed to predict the piRNA-disease associations, named ETGPDA. Specifically, a heterogeneous network is constructed based on the similarity information of piRNA and disease, as well as the known piRNA-disease associations, which is applied to extract low-dimensional embeddings of piRNA and disease based on graph convolutional network with an attention mechanism. Furthermore, the embedding transformation module is developed for the problem of embedding space inconsistency, which is lightweighter, stronger learning ability and higher accuracy. Finally, the piRNA-disease association score is calculated by the similarity of the piRNA and disease embedding. Evaluated by fivefold cross-validation, the AUC of ETGPDA achieves 0.9603, which is better than the other five selected computational models. The case studies based on Head and neck squamous cell carcinoma and Alzheimer's disease further prove the superior performance of ETGPDA. Hence, the ETGPDA is an effective method for predicting the hidden piRNA-disease associations.