ArticleBMC bioinformatics2020
A random forest based computational model for predicting novel lncRNA-disease associations.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 111 citations in OpenAlex.
- Self-supervised learning on graphs predicts non-coding RNA and disease associations.Scientific reports · 2026Article
- Revealing new associations between lncRNAs and diseases through cross attention mechanism and multiple level feature fusion.Scientific reports · 2025Article
- Tensor analysis of animal behavior by matricization and feature selection.Computers in biology and medicine · 2025Article
- Development and validation of a nomogram for predicting low Kt/VBMC nephrology · 2025Article
- ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA-disease associations in heterogeneous graphs.Briefings in bioinformatics · 2024Article
- Prediction of lncRNA and disease associations based on residual graph convolutional networks with attention mechanism.Scientific reports · 2024Article
- Predicting lncRNA-disease associations using multiple metapaths in hierarchical graph attention networks.BMC bioinformatics · 2024Article
- GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations.BMC bioinformatics · 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
- Pattern discovery of long non-coding RNAs associated with the herbal treatments in breast and prostate cancers.Quantitative biology (Beijing, China) · 2023Article
- Unveiling the Comorbidities of Chronic Diseases in Serbia Using ML Algorithms and Kohonen Self-Organizing Maps for Personalized Healthcare Frameworks.Journal of personalized medicine · 2023Article
- iPiDA-SWGCN: Identification of piRNA-disease associations based on Supplementarily Weighted Graph Convolutional Network.PLoS computational biology · 2023Article
- Applying negative sample denoising and multi-view feature for lncRNA-disease association prediction.Frontiers in genetics · 2023Article
- Review
- gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network.BMC bioinformatics · 2022Article
- M2PP: a novel computational model for predicting drug-targeted pathogenic proteins.BMC bioinformatics · 2022Article
- Geometric complement heterogeneous information and random forest for predicting lncRNA-disease associations.Frontiers in genetics · 2022Article
- DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy.Frontiers in neuroscience · 2022Article
- RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation.Molecular genetics and genomics : MGG · 2021Review
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundAccumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources. However, most of the existing models ignore the interference of noisy and redundancy information among these data resources.
resultsTo improve the ability of LDA prediction models, we implemented a random forest and feature selection based LDA prediction model (RFLDA in short). First, the RFLDA integrates the experiment-supported miRNA-disease associations (MDAs) and LDAs, the disease semantic similarity (DSS), the lncRNA functional similarity (LFS) and the lncRNA-miRNA interactions (LMI) as input features. Then, the RFLDA chooses the most useful features to train prediction model by feature selection based on the random forest variable importance score that takes into account not only the effect of individual feature on prediction results but also the joint effects of multiple features on prediction results. Finally, a random forest regression model is trained to score potential lncRNA-disease associations. In terms of the area under the receiver operating characteristic curve (AUC) of 0.976 and the area under the precision-recall curve (AUPR) of 0.779 under 5-fold cross-validation, the performance of the RFLDA is better than several state-of-the-art LDA prediction models. Moreover, case studies on three cancers demonstrate that 43 of the 45 lncRNAs predicted by the RFLDA are validated by experimental data, and the other two predicted lncRNAs are supported by other LDA prediction models.
conclusionsCross-validation and case studies indicate that the RFLDA has excellent ability to identify potential disease-associated lncRNAs.
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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.