The importance of RNA protein-coding gene regulation is by now well appreciated. Noncoding 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.
An efficient graph kernel method for non-coding RNA functional prediction
NAVARIN, NICOLO';
2017
Abstract
The importance of RNA protein-coding gene regulation is by now well appreciated. Noncoding 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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