ArticleBMC bioinformatics2021
A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations.
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 29 papers.
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Who cites it
29 citing papers in PubMed.
- CDELDA: A Content-Based Dual-Encoder for Cold-Start lncRNA-Disease Association Prediction.Biomedicines · 2026Article
- Low-Rank Variational Correction Estimation for Multi-Source Heterogeneous Quantile Linear Regression Models.Statistics in medicine · 2026Article
- SGMHA: semantic graph reconstruction with multi-head attention for gene regulatory network inference.BMC genomics · 2026Article
- CMV-GLA: contrastive multi-view graph layer attention for predicting phosphorylation site-disease associations.BMC bioinformatics · 2026Article
- A model of multi-view contrastive hypergraph learning for predicting circRNA-disease associations.Scientific reports · 2026Article
- HiGLDP: a hierarchical graph neural network for predicting lncRNA-disease associations through multi-omic integration.BMC biology · 2026Article
- CLAP-HMM: a biologically constrained deep learning framework for resistance gene prediction in long DNA sequences.BMC biotechnology · 2025Article
- Target identification of natural products in cancer with chemical proteomics and artificial intelligence approaches.Cancer biology & medicine · 2025Review
- Ethanol Concentration Determination in Baijiu by Graph-Regularized PCA and Random Forest-Based Raman Spectroscopy.ACS omega · 2025Article
- Predicting noncoding RNA and disease associations using multigraph contrastive learning.Scientific reports · 2025Article
- LDAGM: prediction lncRNA-disease asociations by graph convolutional auto-encoder and multilayer perceptron based on multi-view heterogeneous networks.BMC bioinformatics · 2024Article
- Drug repositioning based on residual attention network and free multiscale adversarial training.BMC bioinformatics · 2024Article
- IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.Briefings in bioinformatics · 2024Article
- 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
- GCN-Based Heterogeneous Complex Feature Learning to Enhance Predictability for LncRNA-Disease Associations.ACS omega · 2024Article
- GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations.BMC bioinformatics · 2024Article
- Predicting microbe-disease association based on graph autoencoder and inductive matrix completion with multi-similarities fusion.Frontiers in microbiology · 2024Article
- MNCLCDA: predicting circRNA-drug sensitivity associations by using mixed neighbourhood information and contrastive learning.BMC medical informatics and decision making · 2023Article
- Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments.International journal of molecular sciences · 2023Review
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5 authors.
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Abstract
backgroundNumerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNA-disease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still challenging to learn efficient low-dimensional representations from high-dimensional features of lncRNAs and diseases to predict unknown lncRNA-disease associations accurately.
resultsWe proposed an end-to-end model, VGAELDA, which integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. VGAELDA contains two kinds of graph autoencoders. Variational graph autoencoders (VGAE) infer representations from features of lncRNAs and diseases respectively, while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The integration of both the VGAE for graph representation learning, and the alternate training via variational inference, strengthens the capability of VGAELDA to capture efficient low-dimensional representations from high-dimensional features, and hence promotes the robustness and preciseness for predicting unknown lncRNA-disease associations. Further analysis illuminates that the designed co-training framework of lncRNA and disease for VGAELDA solves a geometric matrix completion problem for capturing efficient low-dimensional representations via a deep learning approach.
conclusionCross validations and numerical experiments illustrate that VGAELDA outperforms the current state-of-the-art methods in lncRNA-disease association prediction. Case studies indicate that VGAELDA is capable of detecting potential lncRNA-disease associations. The source code and data are available at https://github.com/zhanglabNKU/VGAELDA .
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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.