Binding affinity prediction of S. cerevisiae 14-3-3 and GYF peptide-recognition domains using support vector regression

Uslan, Volkan and Seker, Huseyin (2016) Binding affinity prediction of S. cerevisiae 14-3-3 and GYF peptide-recognition domains using support vector regression. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, Piscataway, pp. 3445-3448. ISBN 978-1-4577-0219-8

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Abstract

Proteins interact with other proteins and bio-molecules to carry out biological processes in a cell. Computational models help understanding complex biochemical processes that happens throughout the life of a cell. Domain-mediated protein interaction to peptides one such complex problem in bioinformatics that requires computational predictive models to identify meaningful bindings. In this study, domain-peptide binding affinity prediction models are proposed based on support vector regression. Proposed models are applied to yeast bmh 14-3-3 and syh GYF peptide-recognition domains. The cross validated results of the domain-peptide binding affinity data sets show that predictive performance of the support vector based models are efficient.

Item Type:	Book Section
Subjects:	G900 Others in Mathematical and Computing Sciences
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	06 Feb 2017 16:43
Last Modified:	12 Oct 2019 22:26
URI:	http://nrl.northumbria.ac.uk/id/eprint/29519

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