RNA secondary structure prediction using conditional random fields model
Non-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 Fiel...
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th-cmuir.6653943832-522552018-09-04T09:37:57Z RNA secondary structure prediction using conditional random fields model Sitthichoke Subpaiboonkit Chinae Thammarongtham Robert W. Cutler Jeerayut Chaijaruwanich Biochemistry, Genetics and Molecular Biology Computer Science Social Sciences Non-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. 2018-09-04T09:22:44Z 2018-09-04T09:22:44Z 2013-04-22 Journal 17485681 17485673 2-s2.0-84876225990 10.1504/IJDMB.2013.053195 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84876225990&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/52255 |
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Biochemistry, Genetics and Molecular Biology Computer Science Social Sciences Sitthichoke Subpaiboonkit Chinae Thammarongtham Robert W. Cutler Jeerayut Chaijaruwanich RNA secondary structure prediction using conditional random fields model |
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Non-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. |
format |
Journal |
author |
Sitthichoke Subpaiboonkit Chinae Thammarongtham Robert W. Cutler Jeerayut Chaijaruwanich |
author_facet |
Sitthichoke Subpaiboonkit Chinae Thammarongtham Robert W. Cutler Jeerayut Chaijaruwanich |
author_sort |
Sitthichoke Subpaiboonkit |
title |
RNA secondary structure prediction using conditional random fields model |
title_short |
RNA secondary structure prediction using conditional random fields model |
title_full |
RNA secondary structure prediction using conditional random fields model |
title_fullStr |
RNA secondary structure prediction using conditional random fields model |
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RNA secondary structure prediction using conditional random fields model |
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rna secondary structure prediction using conditional random fields model |
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2018 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84876225990&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/52255 |
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