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dc.contributor.authorWatshara Shoombuatongen_US
dc.contributor.authorHui Ling Huangen_US
dc.contributor.authorJeerayut Chaijaruwanichen_US
dc.contributor.authorPhasit Charoenkwanen_US
dc.contributor.authorHua Chin Leeen_US
dc.contributor.authorShinn Ying Hoen_US
dc.description.abstractMany computational methods have been developed to predict protein crystallization. Most methods use amino acid and dipeptide compositions as part of the informative features. To advance the prediction accuracy, the support vector machine (SVM) based classifiers and ensemble approaches were effective and commonly-used techniques. However, these techniques suffer from the low interpretation ability of insight into crystallization. In this study, we utilize a newly-developed scoring card method (SCM) with a dipeptide composition feature to predict protein crystallization. This SCM classifier obtains prediction results 74%, 0.55 and 0.83 for accuracy, sensitivity and specificity, respectively, which is comparable to the SVM classifier using the same benchmarks. The experimental results show that the SCM classifier has advantages of simplicity, high interpretability, and high accuracy in predicting protein crystallization, compared with existing SVM-basedensemble classifiers. © 2013 IEEE.en_US
dc.subjectComputer Scienceen_US
dc.titlePredicting protein crystallization using a simple scoring card methoden_US
dc.typeConference Proceedingen_US
article.title.sourcetitleProceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013en_US Mai Universityen_US Chiao Tung University Taiwanen_US
Appears in Collections:CMUL: Journal Articles

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