ArticleFrontiers in genetics2019
Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Computational Resources for lncRNA Functions and Targetome.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Pattern discovery of long non-coding RNAs associated with the herbal treatments in breast and prostate cancers.Quantitative biology (Beijing, China) · 2023Article
- lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs.Database : the journal of biological databases and curation · 2023Article
- Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients.Pathology, research and practice · 2023Article
- The hitchhikers' guide to RNA sequencing and functional analysis.Briefings in bioinformatics · 2023Article
- Class similarity network for coding and long non-coding RNA classification.BMC bioinformatics · 2021Article
- Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives.Non-coding RNA · 2020Review
- Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks.BMC bioinformatics · 2020Article
- Computational Methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets.Molecular therapy. Nucleic acids · 2020Article
- Dexmedetomidine had neuroprotective effects on hippocampal neuronal cells via targeting lncRNA SHNG16 mediated microRNA-10b-5p/BDNF axis.Molecular and cellular biochemistry · 2020Article
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Authors and funding
4 authors.
Funding
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Abstract
Many studies have suggested that lncRNAs are involved in distinct and diverse biological processes. The mutation of lncRNAs plays a major role in a wide range of diseases. A comprehensive information of lncRNA-disease associations would improve our understanding of the underlying molecular mechanism that can explain the development of disease. However, the discovery of the relationship between lncRNA and disease in biological experiment is costly and time-consuming. Although many computational algorithms have been proposed in the last decade, there still exists much room to improve because of diverse computational limitations. In this paper, we proposed a deep-learning framework, NNLDA, to predict potential lncRNA-disease associations. We compared it with other two widely-used algorithms on a network with 205,959 interactions between 19,166 lncRNAs and 529 diseases. Results show that NNLDA outperforms other existing algorithm in the prediction of lncRNA-disease association. Additionally, NNLDA can be easily applied to large-scale datasets using the technique of mini-batch stochastic gradient descent. To our best knowledge, NNLDA is the first algorithm that uses deep neural networks to predict lncRNA-disease association. The source code of NNLDA can be freely accessed at https://github.com/gao793583308/NNLDA.
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