ArticleBMC genomics2023
Prediction of lncRNA functions using deep neural networks based on multiple networks.
Article in BMC genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 1 citations in OpenAlex.
- A novel deep learning-driven framework for improving lncRNA comprehensive annotation with LncADeep 2.0.Bioinformatics (Oxford, England) · 2026Article
- Long Noncoding RNA function prediction via multiview cross-contrastive learning combined with multiscale semantic adaptive optimization.Briefings in bioinformatics · 2025Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundMore and more studies show that lncRNA is widely involved in various physiological processes of the organism. However, the functions of the vast majority of them continue to be unknown. In addition, data related to lncRNAs in biological databases are constantly increasing. Therefore, it is quite urgent to develop a computing method to make the utmost of these data.
resultsIn this paper, we propose a new computational method based on global heterogeneous networks to predict the functions of lncRNAs, called DNGRGO. DNGRGO first calculates the similarities among proteins, miRNAs, and lncRNAs, and annotates the functions of lncRNAs according to its similar protein-coding genes, which have been labeled with gene ontology (GO). To evaluate the performance of DNGRGO, we manually annotated GO terms to lncRNAs and implemented our method on these data. Compared with the existing methods, the results of DNGRGO show superior predictive performance of maximum F-measure and coverage.
conclusionsDNGRGO is able to annotate lncRNAs through capturing the low-dimensional features of the heterogeneous network. Moreover, the experimental results show that integrating miRNA data can help to improve the predictive performance of DNGRGO.
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