RNA secondary structure prediction using conditional random fields model

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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Main Authors: Sitthichoke Subpaiboonkit, Chinae Thammarongtham, Robert W. Cutler, Jeerayut Chaijaruwanich
Format: Journal
Published: 2018
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84876225990&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/48004
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Institution: Chiang Mai University
id th-cmuir.6653943832-48004
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spelling th-cmuir.6653943832-480042018-04-25T08:46:33Z RNA secondary structure prediction using conditional random fields model Sitthichoke Subpaiboonkit Chinae Thammarongtham Robert W. Cutler Jeerayut Chaijaruwanich 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. 2018-04-25T08:46:33Z 2018-04-25T08:46:33Z 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/6653943832/48004
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description 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
spellingShingle Sitthichoke Subpaiboonkit
Chinae Thammarongtham
Robert W. Cutler
Jeerayut Chaijaruwanich
RNA secondary structure prediction using conditional random fields model
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
title_full_unstemmed RNA secondary structure prediction using conditional random fields model
title_sort rna secondary structure prediction using conditional random fields model
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84876225990&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/48004
_version_ 1681423169008697344

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