Unsupervised selection of RV144 HIV vaccine-induced antibody features correlated to natural killer cell-mediated cytotoxic reactions

Sarac, Ferdi, Uslan, Volkan, Seker, Huseyin and Bouridane, Ahmed (2016) Unsupervised selection of RV144 HIV vaccine-induced antibody features correlated to natural killer cell-mediated cytotoxic reactions. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, Piscataway, pp. 3072-3075. ISBN 978-1-4577-0219-8

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Official URL: http://dx.doi.org/10.1109/EMBC.2016.7591378

Abstract

HIV-1 vaccine injection has been shown less effective due to the diversity of antigens. Increasing the knowledge of the associations between immune system and virus would ultimately result in producing effective vaccines against HIV-1 virus. To increase the understanding of immunological information, computational models can be utilised to construct predictive models. The aim of this study is, therefore, to predict the effect of antibody features (IgGs) and primary Natural Killing (NK) cells' cytotoxic activities on RV144 vaccine recipients and to disclose the functional relationship between immune system and HIV virus. The RV144 vaccine data set contains 100 data samples in which 20 of them are the placebo samples and 80 of them are the vaccine injected samples. Each data sample has twenty antibody features that consist of features related to IgG subclass and antigen specificity. In this paper, five different unsupervised feature selection methods (USFSMs) are utilised in order to identify the discriminating antibody features as USFSMs are regarded as unbiased approach. Then, the support vector based methods are utilised to assess association between cellular cytotoxicity by Natural Killer (NK) cells and cells that release glycoprotein (gp)120 antibody. The results yield high correlation coefficient as much as 0.48 and 0.65 for classificationthe support vector regression (SVR) and classification (SVM) predictive models, respectively.

Item Type: Book Section
Subjects: B900 Others in Subjects allied to Medicine
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 07 Feb 2017 11:40
Last Modified: 07 Feb 2017 11:40
URI: http://nrl.northumbria.ac.uk/id/eprint/29518

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