ArticleBMC bioinformatics2022
gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- HiGLDP: a hierarchical graph neural network for predicting lncRNA-disease associations through multi-omic integration.BMC biology · 2026Article
- Dynamic spatiotemporal graph attention networks for cross-regional multi-disease forecasting and intervention optimization.Frontiers in public health · 2026Article
- A lightweight single-view contrastive learning hypergraph neural network for food-microbe-disease association prediction.BMC bioinformatics · 2025Article
- Bind: large-scale biological interaction network discovery through knowledge graph-driven machine learning.Journal of translational medicine · 2025Article
- LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting.BMC bioinformatics · 2025Article
- PanGIA: A universal framework for identifying association between ncRNAs and diseases.GigaScience · 2025Article
- Predicting noncoding RNA and disease associations using multigraph contrastive learning.Scientific reports · 2025Article
- A novel end-to-end learning framework for inferring lncRNA-disease associations based on convolution neural network.Frontiers in genetics · 2025Article
- Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer.Computational and structural biotechnology journal · 2024Article
- Therapeutic gene target prediction using novel deep hypergraph representation learning.Briefings in bioinformatics · 2024Article
- Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer.bioRxiv : the preprint server for biology · 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
- Predict lncRNA-drug associations based on graph neural network.Frontiers in genetics · 2024Article
- Review
- Predicting lncRNA-disease associations based on heterogeneous graph convolutional generative adversarial network.PLoS computational biology · 2023Article
- Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments.International journal of molecular sciences · 2023Review
- lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs.Database : the journal of biological databases and curation · 2023Article
- The hitchhikers' guide to RNA sequencing and functional analysis.Briefings in bioinformatics · 2023Article
- A lncRNA-disease association prediction tool development based on bridge heterogeneous information network via graph representation learning for family medicine and primary care.Frontiers in genetics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundLong non-coding RNAs (lncRNAs) are related to human diseases by regulating gene expression. Identifying lncRNA-disease associations (LDAs) will contribute to diagnose, treatment, and prognosis of diseases. However, the identification of LDAs by the biological experiments is time-consuming, costly and inefficient. Therefore, the development of efficient and high-accuracy computational methods for predicting LDAs is of great significance.
resultsIn this paper, we propose a novel computational method (gGATLDA) to predict LDAs based on graph-level graph attention network. Firstly, we extract the enclosing subgraphs of each lncRNA-disease pair. Secondly, we construct the feature vectors by integrating lncRNA similarity and disease similarity as node attributes in subgraphs. Finally, we train a graph neural network (GNN) model by feeding the subgraphs and feature vectors to it, and use the trained GNN model to predict lncRNA-disease potential association scores. The experimental results show that our method can achieve higher area under the receiver operation characteristic curve (AUC), area under the precision recall curve (AUPR), accuracy and F1-Score than the state-of-the-art methods in five fold cross-validation. Case studies show that our method can effectively identify lncRNAs associated with breast cancer, gastric cancer, prostate cancer, and renal cancer.
conclusionThe experimental results indicate that our method is a useful approach for predicting potential LDAs.
Indexed as
Identifiers
What OpenQuestion holds
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.