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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/35622
Title: Sequence based human leukocyte antigen gene prediction using informative physicochemical properties
Authors: Watshara Shoombuatong
Panuwat Mekha
Jeerayut Chaijaruwanich
Mahidol University
Maejo University
Chiang Mai University
Keywords: Biochemistry, Genetics and Molecular Biology;Computer Science
Issue Date: 1-Jan-2015
Citation: International Journal of Data Mining and Bioinformatics. Vol.13, No.3 (2015), 211-224
Abstract: Copyright © 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.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84943425242&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/35622
ISSN: 17485681
17485673
Appears in Collections:Scopus 2011-2015

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