An efficient graph kernel method for non-coding RNA functional prediction | Zendy

Nicolò Navarin | Zendy; Fabrizio Costa | Zendy

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An efficient graph kernel method for non-coding RNA functional prediction

Author(s) -

Nicolò Navarin,

Fabrizio Costa

Publication year - 2017

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/btx295

Subject(s) - computer science , scalability , exploit , non coding rna , graph , source code , annotation , coding (social sciences) , kernel (algebra) , artificial intelligence , machine learning , computational biology , theoretical computer science , rna , gene , biology , database , programming language , genetics , statistics , computer security , mathematics , combinatorics

The importance of RNA protein-coding gene regulation is by now well appreciated. Non-coding RNAs (ncRNAs) are known to regulate gene expression at practically every stage, ranging from chromatin packaging to mRNA translation. However the functional characterization of specific instances remains a challenging task in genome scale settings. For this reason, automatic annotation approaches are of interest. Existing computational methods are either efficient but non-accurate or they offer increased precision, but present scalability problems.

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