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dc.contributor.authorWatshara Shoombuatongen_US
dc.contributor.authorPatrinee Traisathiten_US
dc.contributor.authorSukon Prasitwattanasereeen_US
dc.contributor.authorChatchai Tayapiwatanaen_US
dc.contributor.authorRobert Cutleren_US
dc.contributor.authorJeerayut Chaijaruwanichen_US
dc.description.abstractThe formation of disulphide bonds between cysteines plays a major role in protein folding, structure, function and evolution. Many computational approaches have been used to predict the disulphide bonding state of cysteines. In our work, we developed a novel method based on Conditional Random Fields (CRFs) to predict the disulphide bonding state from protein primary sequence, predicted secondary structures and predicted relative solvent accessibilities (all-state information). Our experiments obtain 84% accuracy, 88% precision and 94% recall, using all-state information. However, our results show essentially identical results when using protein sequence and predicted relative solvent accessibilities in the absence of secondary structure. © 2011 Inderscience Enterprises Ltd.en_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.subjectComputer Scienceen_US
dc.subjectSocial Sciencesen_US
dc.titlePrediction of the disulphide bonding state of cysteines in proteins using Conditional Random Fieldsen_US
article.title.sourcetitleInternational Journal of Data Mining and Bioinformaticsen_US
article.volume5en_US Mai Universityen_US National Science and Technology Development Agencyen_US
Appears in Collections:CMUL: Journal Articles

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