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dc.contributor.authorSitthichoke Subpaiboonkiten_US
dc.contributor.authorChinae Thammarongthamen_US
dc.contributor.authorRobert W. Cutleren_US
dc.contributor.authorJeerayut Chaijaruwanichen_US
dc.description.abstractNon-coding RNAs (ncRNAs) have important biological functions in living cells dependent on their conserved secondary structures. Here, we focus on computational RNA secondary structure prediction by exploring primary sequences and complementary base pair interactions using the Conditional Random Fields (CRFs) model, which treats RNA prediction as a sequence labelling problem. Proposing suitable feature extraction from known RNA secondary structures, we developed a feature extraction based on natural RNA's loop and stem characteristics. Our CRFs models can predict the secondary structures of the test RNAs with optimal F-score prediction between 56.61 and 98.20% for different RNA families. Copyright © 2013 Inderscience Enterprises Ltd.en_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.subjectComputer Scienceen_US
dc.subjectSocial Sciencesen_US
dc.titleRNA secondary structure prediction using conditional random fields modelen_US
article.title.sourcetitleInternational Journal of Data Mining and Bioinformaticsen_US
article.volume7en_US Mai Universityen_US National Center for Genetic Engineering and Biotechnologyen_US Research Scientisten_US
Appears in Collections:CMUL: Journal Articles

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