ArticlePLoS computational biology2023
iPiDA-SWGCN: Identification of piRNA-disease associations based on Supplementarily Weighted Graph Convolutional Network.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Advanced artificial intelligence in piRNA and PIWI-like protein research: A systematic review of recurrent neural networks, long short-term memory, and emerging computational techniquesBiomedica : revista del Instituto Nacional de Salud · 2025Pooled it
- Predicting piRNA-Disease Associations Based on Dual-View Learning and Multi-head Self-Attention Mechanism Fusion.Interdisciplinary sciences, computational life sciences · 2026Article
- MambaCAttnGCN+: a comprehensive framework integrating MambaTextCNN, cross-attention and graph convolution network for piRNA-disease association prediction.Scientific reports · 2025Article
- Article
- 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
- Review
- PUTransGCN: identification of piRNA-disease associations based on attention encoding graph convolutional network and positive unlabelled learning.Briefings in bioinformatics · 2024Article
- PPDAMEGCN: Predicting piRNA-Disease Associations Based on Multi-Edge Type Graph Convolutional Network.IET systems biologyArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately identifying potential piRNA-disease associations is of great importance in uncovering the pathogenesis of diseases. Recently, several machine-learning-based methods have been proposed for piRNA-disease association detection. However, they are suffering from the high sparsity of piRNA-disease association network and the Boolean representation of piRNA-disease associations ignoring the confidence coefficients. In this study, we propose a supplementarily weighted strategy to solve these disadvantages. Combined with Graph Convolutional Networks (GCNs), a novel predictor called iPiDA-SWGCN is proposed for piRNA-disease association prediction. There are three main contributions of iPiDA-SWGCN: (i) Potential piRNA-disease associations are preliminarily supplemented in the sparse piRNA-disease network by integrating various basic predictors to enrich network structure information. (ii) The original Boolean piRNA-disease associations are assigned with different relevance confidence to learn node representations from neighbour nodes in varying degrees. (iii) The experimental results show that iPiDA-SWGCN achieves the best performance compared with the other state-of-the-art methods, and can predict new piRNA-disease associations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.