ArticleBioinformatics (Oxford, England)2018
IRSOM, a reliable identifier of ncRNAs based on supervised self-organizing maps with rejection.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- HyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction.RNA biology · 2026Article
- Benchmarking reveals the superiority of nucleic acid foundation models in predicting lncRNA coding potential.Genome biology · 2026Article
- Article
- TetraRNA, a tetra-class machine learning model for deciphering the coding potential derivation of RNA world.Computational and structural biotechnology journal · 2025Article
- A survey of experimental and computational identification of small proteins.Briefings in bioinformatics · 2024Review
- IRSOM2: a web server for predicting bifunctional RNAs.Nucleic acids research · 2023Article
- Revealing the small proteome ofmicroLife · 2023Article
- A3SOM, abstained explainable semi-supervised neural network based on self-organizing map.PloS one · 2023Article
- Deep learning tools are top performers in long non-coding RNA prediction.Briefings in functional genomics · 2022Article
- RiboReport - benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria.Briefings in bioinformatics · 2022Article
- Silencing of Curlin Protein via M13 Phagemid-Mediated Synthetic sRNA Expression Reduces Virulence in the Avian Pathogenic E. coli (APEC).Current microbiology · 2022Article
- The computational approaches of lncRNA identification based on coding potential:Computational and structural biotechnology journal · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Non-coding RNAs (ncRNAs) play important roles in many biological processes and are involved in many diseases. Their identification is an important task, and many tools exist in the literature for this purpose. However, almost all of them are focused on the discrimination of coding and ncRNAs without giving more biological insight. In this paper, we propose a new reliable method called IRSOM, based on a supervised Self-Organizing Map (SOM) with a rejection option, that overcomes these limitations. The rejection option in IRSOM improves the accuracy of the method and also allows identifing the ambiguous transcripts. Furthermore, with the visualization of the SOM, we analyze the rejected predictions and highlight the ambiguity of the transcripts. Results: IRSOM was tested on datasets of several species from different reigns, and shown better results compared to state-of-art. The accuracy of IRSOM is always greater than 0.95 for all the species with an average specificity of 0.98 and an average sensitivity of 0.99. Besides, IRSOM is fast (it takes around 254 s to analyze a dataset of 147 000 transcripts) and is able to handle very large datasets. Availability and implementation: IRSOM is implemented in Python and C++. It is available on our software platform EvryRNA (http://EvryRNA.ibisc.univ-evry.fr).
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