ArticlePloS one2012
Computational prediction of polycomb-associated long non-coding RNAs.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Non-coding RNAs: modulators of signaling pathways and prognostic biomarkers in digestive system cancer.Molecular cancer · 2026Review
- The pleiotropic roles of circular and long noncoding RNAs in cutaneous melanoma.Molecular oncology · 2022Review
- Comparing biological information contained in mRNA and non-coding RNAs for classification of lung cancer patients.BMC cancer · 2019Article
- The PRC2-binding long non-coding RNAs in human and mouse genomes are associated with predictive sequence features.Scientific reports · 2017Article
- A Review on Recent Computational Methods for Predicting Noncoding RNAs.BioMed research international · 2017Review
- Transposable Element Insertions in Long Intergenic Non-Coding RNA Genes.Frontiers in bioengineering and biotechnology · 2015Article
- Non-coding yet non-trivial: a review on the computational genomics of lincRNAs.BioData mining · 2015Review
- Databases for lncRNAs: a comparative evaluation of emerging tools.RNA (New York, N.Y.) · 2014Review
- Role of non-coding RNAs in pancreatic cancer: the bane of the microworld.World journal of gastroenterology · 2014Review
- The RIDL hypothesis: transposable elements as functional domains of long noncoding RNAs.RNA (New York, N.Y.) · 2014Article
- The non-coding transcriptome as a dynamic regulator of cancer metastasis.Cancer metastasis reviews · 2014Review
- Computational approaches in detecting non- coding RNA.Current genomics · 2013Article
- ANRIL: molecular mechanisms and implications in human health.International journal of molecular sciences · 2013Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Among thousands of long non-coding RNAs (lncRNAs) only a small subset is functionally characterized and the functional annotation of lncRNAs on the genomic scale remains inadequate. In this study we computationally characterized two functionally different parts of human lncRNAs transcriptome based on their ability to bind the polycomb repressive complex, PRC2. This classification is enabled by the fact that while all lncRNAs constitute a diverse set of sequences, the classes of PRC2-binding and PRC2 non-binding lncRNAs possess characteristic combinations of sequence-structure patterns and, therefore, can be separated within the feature space. Based on the specific combination of features, we built several machine-learning classifiers and identified the SVM-based classifier as the best performing. We further showed that the SVM-based classifier is able to generalize on the independent data sets. We observed that this classifier, trained on the human lncRNAs, can predict up to 59.4% of PRC2-binding lncRNAs in mice. This suggests that, despite the low degree of sequence conservation, many lncRNAs play functionally conserved biological roles.
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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.