ArticlePloS one2014
A computational framework to infer human disease-associated long noncoding RNAs.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 2 of them syntheses that pooled it.
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Who cites it
66 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- TUG1, SPRY4-IT1, and HULC as valuable prognostic biomarkers of survival in cancer: A PRISMA-compliant meta-analysis.Medicine · 2017Pooled it
- Prognostic value of long noncoding RNA MALAT1 in various carcinomas: evidence from nine studies.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2016Pooled it
- DSGCNLDA: A Multi-view Learning Model with DualScope Attention for lncRNA-Disease Association Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- A novel end-to-end learning framework for inferring lncRNA-disease associations based on convolution neural network.Frontiers in genetics · 2025Article
- Predicting lncRNA-Disease Associations Based on a Dual-Path Feature Extraction Network with Multiple Sources of Information Integration.ACS omega · 2024Article
- Prediction of lncRNA and disease associations based on residual graph convolutional networks with attention mechanism.Scientific reports · 2024Article
- Association filtering and generative adversarial networks for predicting lncRNA-associated disease.BMC bioinformatics · 2023Article
- lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering.Scientific reports · 2022Article
- A learning-based method to predict LncRNA-disease associations by combining CNN and ELM.BMC bioinformatics · 2022Article
- Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network.Frontiers in genetics · 2022Article
- Geometric complement heterogeneous information and random forest for predicting lncRNA-disease associations.Frontiers in genetics · 2022Article
- SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec.BMC bioinformatics · 2021Article
- RNA-Seq Implies Divergent Regulation Patterns of LincRNA on Spermatogenesis and Testis Growth in Goats.Animals : an open access journal from MDPI · 2021Article
- A novel computational model for predicting potential LncRNA-disease associations based on both direct and indirect features of LncRNA-disease pairs.BMC bioinformatics · 2020Article
- Cluster correlation based method for lncRNA-disease association prediction.BMC bioinformatics · 2020Article
- Novel biomarkers for the diagnosis and prognosis of colorectal cancer.Intestinal research · 2020Review
- 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
- Reveal the Regulation Patterns of Prognosis-Related miRNAs and lncRNAs Across Solid Tumors in the Cancer Genome Atlas.Frontiers in cell and developmental biology · 2020Article
- Multiview Consensus Graph Learning for lncRNA-Disease Association Prediction.Frontiers in genetics · 2020Article
6 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
As a major class of noncoding RNAs, long noncoding RNAs (lncRNAs) have been implicated in various critical biological processes. Accumulating researches have linked dysregulations and mutations of lncRNAs to a variety of human disorders and diseases. However, to date, only a few human lncRNAs have been associated with diseases. Therefore, it is very important to develop a computational method to globally predict potential associated diseases for human lncRNAs. In this paper, we developed a computational framework to accomplish this by combining human lncRNA expression profiles, gene expression profiles, and human disease-associated gene data. Applying this framework to available human long intergenic noncoding RNAs (lincRNAs) expression data, we showed that the framework has reliable accuracy. As a result, for non-tissue-specific lincRNAs, the AUC of our algorithm is 0.7645, and the prediction accuracy is about 89%. This study will be helpful for identifying novel lncRNAs for human diseases, which will help in understanding the roles of lncRNAs in human diseases and facilitate treatment. The corresponding codes for our method and the predicted results are all available at http://asdcd.amss.ac.cn/MingXiLiu/lncRNA-disease.html.
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