ArticleBMC genomics2023
ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network.
Article in BMC genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- LLM4PDA: Augmenting piRNA-Disease Association Prediction with Large Language Models.Interdisciplinary sciences, computational life sciences · 2026Article
- Predicting piRNA-Disease Associations Based on Dual-View Learning and Multi-head Self-Attention Mechanism Fusion.Interdisciplinary sciences, computational life sciences · 2026Article
- AGAPIR: A Novel PIWI-Interacting RNA Enhancing Post-Decompression Angiogenesis in Degenerative Cervical Myelopathy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- MambaCAttnGCN+: a comprehensive framework integrating MambaTextCNN, cross-attention and graph convolution network for piRNA-disease association prediction.Scientific reports · 2025Article
- Article
- NRGCNMDA: Microbe-Drug Association Prediction Based on Residual Graph Convolutional Networks and Conditional Random Fields.Interdisciplinary sciences, computational life sciences · 2025Article
- iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations.BMC biology · 2025Article
- Databases and computational methods for the identification of piRNA-related molecules: A survey.Computational and structural biotechnology journal · 2024Review
- MRDPDA: A multi-Laplacian regularized deepFM model for predicting piRNA-disease associations.Journal of cellular and molecular medicine · 2024Article
- PUTransGCN: identification of piRNA-disease associations based on attention encoding graph convolutional network and positive unlabelled learning.Briefings in bioinformatics · 2024Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundPiwi-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.
methodsIn 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.
resultsEvaluated 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.
conclusionsHence, the ETGPDA is an effective method for predicting the hidden piRNA-disease associations.
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