IRSOM, a reliable identifier of ncRNAs based on supervised self-organizing maps with rejection | Zendy

Ludovic Platon | Zendy; Farida Zehraoui | Zendy; Abdelhafid Bendahmane | Zendy; Fariza Tahi | Zendy

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IRSOM, a reliable identifier of ncRNAs based on supervised self-organizing maps with rejection

Author(s) -

Ludovic Platon,

Farida Zehraoui,

Abdelhafid Bendahmane,

Fariza Tahi

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty572

Subject(s) - python (programming language) , identifier , computer science , ambiguity , coding (social sciences) , identification (biology) , visualization , machine learning , software , data mining , artificial intelligence , biology , mathematics , programming language , operating system , statistics , botany

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.

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