ArticlePLoS computational biology2022
iPiDA-GCN: Identification of piRNA-disease associations based on Graph Convolutional Network.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 42 citations in OpenAlex.
- Predicting piRNA-Disease Associations Based on Dual-View Learning and Multi-head Self-Attention Mechanism Fusion.Interdisciplinary sciences, computational life sciences · 2026Article
- SPGA: graph representation learning and attention fusion for enhanced disease-associated snoRNA prediction.BMC bioinformatics · 2026Article
- ESAE-SDA: ensemble sparse autoencoder framework for epigenomics-informed snoRNA-disease associations prediction.BMC bioinformatics · 2025Article
- Contrastive hypergraph collaborative filtering for transfer RNA-disease association prediction.Briefings in bioinformatics · 2025Article
- 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
- Unraveling Dysregulated Cell Signaling Pathways, Genetic and Epigenetic Mysteries of Parkinson's Disease.Molecular neurobiology · 2024Review
- MRDPDA: A multi-Laplacian regularized deepFM model for predicting piRNA-disease associations.Journal of cellular and molecular medicine · 2024Article
- An ontology-based knowledge graph for representing interactions involving RNA molecules.Scientific data · 2024Article
- Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration.ACS pharmacology & translational science · 2024Review
- PUTransGCN: identification of piRNA-disease associations based on attention encoding graph convolutional network and positive unlabelled learning.Briefings in bioinformatics · 2024Article
- IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.Briefings in bioinformatics · 2024Article
- piRNA-disease association prediction based on multi-channel graph variational autoencoder.PeerJ. Computer science · 2024Article
- Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines.Frontiers in pharmacology · 2024Article
- AutoEdge-CCP: A novel approach for predicting cancer-associated circRNAs and drugs based on automated edge embedding.PLoS computational biology · 2024Article
- RMDGCN: Prediction of RNA methylation and disease associations based on graph convolutional network with attention mechanism.PLoS computational biology · 2023Article
- iPiDA-SWGCN: Identification of piRNA-disease associations based on Supplementarily Weighted Graph Convolutional Network.PLoS computational biology · 2023Article
- ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network.BMC genomics · 2023Article
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
motivationPiwi-interacting RNAs (piRNAs) play a critical role in the progression of various diseases. Accurately identifying the associations between piRNAs and diseases is important for diagnosing and prognosticating diseases. Although some computational methods have been proposed to detect piRNA-disease associations, it is challenging for these methods to effectively capture nonlinear and complex relationships between piRNAs and diseases because of the limited training data and insufficient association representation.
resultsWith the growth of piRNA-disease association data, it is possible to design a more complex machine learning method to solve this problem. In this study, we propose a computational method called iPiDA-GCN for piRNA-disease association identification based on graph convolutional networks (GCNs). The iPiDA-GCN predictor constructs the graphs based on piRNA sequence information, disease semantic information and known piRNA-disease associations. Two GCNs (Asso-GCN and Sim-GCN) are used to extract the features of both piRNAs and diseases by capturing the association patterns from piRNA-disease interaction network and two similarity networks. GCNs can capture complex network structure information from these networks, and learn discriminative features. Finally, the full connection networks and inner production are utilized as the output module to predict piRNA-disease association scores. Experimental results demonstrate that iPiDA-GCN achieves better performance than the other state-of-the-art methods, benefitted from the discriminative features extracted by Asso-GCN and Sim-GCN. The iPiDA-GCN predictor is able to detect new piRNA-disease associations to reveal the potential pathogenesis at the RNA level. The data and source code are available at http://bliulab.net/iPiDA-GCN/.
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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.