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dc.contributor.authorWatshara Shoombuatongen_US
dc.contributor.authorPanuwat Mekhaen_US
dc.contributor.authorJeerayut Chaijaruwanichen_US
dc.description.abstractCopyright © 2015 Inderscience Enterprises Ltd. Prediction of different classes within the human leukocyte antigen (HLA) gene family can provide insight into the human immune system and its response to viral pathogens. Therefore, it is desirable to develop an efficient and easily interpretable method for predicting HLA gene class compared to existing methods. We investigated the HLA gene prediction problem as follows: (a) establishing a dataset (HLA262) such that the sequence identity of the complete HLA dataset was reduced to 30%; (b) proposing a feature set of informative physicochemical properties that cooperate with SVM (named HLAPred) to achieve high accuracy and sensitivity (90.04% and 82.99%, respectively) compared with existing methods; and (c) analysing the informative physicochemical properties to understand the physicochemical properties and molecular mechanisms of the HLA gene family.en_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.subjectComputer Scienceen_US
dc.subjectSocial Sciencesen_US
dc.titleSequence based human leukocyte antigen gene prediction using informative physicochemical propertiesen_US
article.title.sourcetitleInternational Journal of Data Mining and Bioinformaticsen_US
article.volume13en_US Universityen_US Universityen_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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