ArticleBMC bioinformatics2021
BiGAN: LncRNA-disease association prediction based on bidirectional generative adversarial network.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 41 citations in OpenAlex.
- Predicting lncRNA-disease associations using multiple metapaths in hierarchical graph attention networks.BMC bioinformatics · 2024Article
- Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction.BMC genomics · 2024Article
- GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations.BMC bioinformatics · 2024Article
- Predict lncRNA-drug associations based on graph neural network.Frontiers in genetics · 2024Article
- LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine.Briefings in bioinformatics · 2023Article
- Predicting lncRNA-disease associations based on heterogeneous graph convolutional generative adversarial network.PLoS computational biology · 2023Article
- Association filtering and generative adversarial networks for predicting lncRNA-associated disease.BMC bioinformatics · 2023Article
- Predicting potential lncRNA biomarkers for lung cancer and neuroblastoma based on an ensemble of a deep neural network and LightGBM.Frontiers in genetics · 2023Article
- MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model.BMC bioinformatics · 2022Article
- gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network.BMC bioinformatics · 2022Article
- Recent advances in machine learning methods for predicting LncRNA and disease associations.Frontiers in cellular and infection microbiology · 2022Review
- Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases.Frontiers in genetics · 2022Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAn increasing number of studies have shown that lncRNAs are crucial for the control of hormones and the regulation of various physiological processes in the human body, and deletion mutations in RNA are related to many human diseases. LncRNA- disease association prediction is very useful for understanding pathogenesis, diagnosis, and prevention of diseases, and is helpful for labelling relevant biological information.
resultsIn this manuscript, we propose a computational model named bidirectional generative adversarial network (BiGAN), which consists of an encoder, a generator, and a discriminator to predict new lncRNA-disease associations. We construct features between lncRNA and disease pairs by utilizing the disease semantic similarity, lncRNA sequence similarity, and Gaussian interaction profile kernel similarities of lncRNAs and diseases. The BiGAN maps the latent features of similarity features to predict unverified association between lncRNAs and diseases. The computational results have proved that the BiGAN performs significantly better than other state-of-the-art approaches in cross-validation. We employed the proposed model to predict candidate lncRNAs for renal cancer and colon cancer. The results are promising. Case studies show that almost 70% of lncRNAs in the top 10 prediction lists are verified by recent biological research.
conclusionThe experimental results indicated that our proposed model had an accurate predictive ability for the association of lncRNA-disease pairs.
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