ArticleBMC bioinformatics2018
Measures of co-expression for improved function prediction of long non-coding RNAs.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Expression regulation network in papillae of sea cucumbers: Whole-transcriptome and DNA methylation datasets.Scientific data · 2025Article
- Integration of transcription regulation and functional genomic data reveals lncRNA SNHG6's role in hematopoietic differentiation and leukemia.Journal of biomedical science · 2024Article
- LncRP11-675F6.3 responds to rapamycin treatment and reduces triglyceride accumulation via interacting with HK1 in hepatocytes by regulating autophagy and VLDL-related proteins.Acta biochimica et biophysica Sinica · 2023Article
- Article
- RNA editing regulates lncRNA splicing in human early embryo development.PLoS computational biology · 2021Article
- DeepLPI: a multimodal deep learning method for predicting the interactions between lncRNAs and protein isoforms.BMC bioinformatics · 2021Article
- Identification and Annotation of Potential Function of Regulatory Antisense Long Non-Coding RNAs Related to Feed Efficiency inInternational journal of molecular sciences · 2020Article
- Enhanced identification of significant regulators of gene expression.BMC bioinformatics · 2020Article
- Endothelial function and dysfunction in the cardiovascular system: the long non-coding road.Cardiovascular research · 2019Review
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Authors and funding
2 authors.
Funding
Abstract
backgroundAlmost 16,000 human long non-coding RNA (lncRNA) genes have been identified in the GENCODE project. However, the function of most of them remains to be discovered. The function of lncRNAs and other novel genes can be predicted by identifying significantly enriched annotation terms in already annotated genes that are co-expressed with the lncRNAs. However, such approaches are sensitive to the methods that are used to estimate the level of co-expression.
resultsWe have tested and compared two well-known statistical metrics (Pearson and Spearman) and two geometrical metrics (Sobolev and Fisher) for identification of the co-expressed genes, using experimental expression data across 19 normal human tissues. We have also used a benchmarking approach based on semantic similarity to evaluate how well these methods are able to predict annotation terms, using a well-annotated set of protein-coding genes.
conclusionThis work shows that geometrical metrics, in particular in combination with the statistical metrics, will predict annotation terms more efficiently than traditional approaches. Tests on selected lncRNAs confirm that it is possible to predict the function of these genes given a reliable set of expression data. The software used for this investigation is freely available.
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