ArticleScientific reports2017
Global network random walk for predicting potential human lncRNA-disease associations.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed, 68 citations in OpenAlex.
- APDCA: An accurate and effective method for predicting associations between RBPs and AS-events during epithelial-mesenchymal transition.PLoS computational biology · 2025Article
- Neighborhood-Regularized Matrix Factorization for lncRNA-Disease Association Identification.International journal of molecular sciences · 2025Article
- Discovery and functional characterization of LncRNAs associated with inflammation and macrophage activation.Methods (San Diego, Calif.) · 2024Review
- Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction.BMC genomics · 2024Article
- RMDGCN: Prediction of RNA methylation and disease associations based on graph convolutional network with attention mechanism.PLoS computational biology · 2023Article
- iLncDA-RSN: identification of lncRNA-disease associations based on reliable similarity networks.Frontiers in genetics · 2023Article
- gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network.BMC bioinformatics · 2022Article
- Prediction of lncRNA-disease association based on a Laplace normalized random walk with restart algorithm on heterogeneous networks.BMC bioinformatics · 2022Article
- Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer.Frontiers in genetics · 2021Review
- Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview.Frontiers in genetics · 2021Review
- Predicting LncRNA-Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks.Frontiers in genetics · 2021Article
- Non-coding RNAs underlying chemoresistance in gastric cancer.Cellular oncology (Dordrecht, Netherlands) · 2020Review
- LDNFSGB: prediction of long non-coding rna and disease association using network feature similarity and gradient boosting.BMC bioinformatics · 2020Article
- A random forest based computational model for predicting novel lncRNA-disease associations.BMC bioinformatics · 2020Article
- LDAI-ISPS: LncRNA-Disease Associations Inference Based on Integrated Space Projection Scores.International journal of molecular sciences · 2020Article
- Identifying Cancer-Related lncRNAs Based on a Convolutional Neural Network.Frontiers in cell and developmental biology · 2020Article
- GBDTL2E: Predicting lncRNA-EF Associations Using Diffusion and HeteSim Features Based on a Heterogeneous Network.Frontiers in genetics · 2020Article
- LncDisAP: a computation model for LncRNA-disease association prediction based on multiple biological datasets.BMC bioinformatics · 2019Article
- LDAPred: A Method Based on Information Flow Propagation and a Convolutional Neural Network for the Prediction of Disease-Associated lncRNAs.International journal of molecular sciences · 2019Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is more and more evidence that the mutation and dysregulation of long non-coding RNA (lncRNA) are associated with numerous diseases, including cancers. However, experimental methods to identify associations between lncRNAs and diseases are expensive and time-consuming. Effective computational approaches to identify disease-related lncRNAs are in high demand; and would benefit the detection of lncRNA biomarkers for disease diagnosis, treatment, and prevention. In light of some limitations of existing computational methods, we developed a global network random walk model for predicting lncRNA-disease associations (GrwLDA) to reveal the potential associations between lncRNAs and diseases. GrwLDA is a universal network-based method and does not require negative samples. This method can be applied to a disease with no known associated lncRNA (isolated disease) and to lncRNA with no known associated disease (novel lncRNA). The leave-one-out cross validation (LOOCV) method was implemented to evaluate the predicted performance of GrwLDA. As a result, GrwLDA obtained reliable AUCs of 0.9449, 0.8562, and 0.8374 for overall, novel lncRNA and isolated disease prediction, respectively, significantly outperforming previous methods. Case studies of colon, gastric, and kidney cancers were also implemented, and the top 5 disease-lncRNA associations were reported for each disease. Interestingly, 13 (out of the 15) associations were confirmed by literature mining.
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.