Quantitative prediction of peptide binding affinity by using hybrid fuzzy support vector regression

Volkan Uslan; Huseyin Seker

doi:10.1016/j.asoc.2016.01.024

Quantitative prediction of peptide binding affinity by using hybrid fuzzy support vector regression

Volkan Uslan, Huseyin Seker^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

19 Citations (SciVal)

Original language	English
Pages (from-to)	210-221
Number of pages	12
Journal	Applied Soft Computing
Volume	43
DOIs	https://doi.org/10.1016/j.asoc.2016.01.024
Publication status	Published (VoR) - 1 Jun 2016

Funding

During this study, Volkan Uslan was funded by De Montfort University Leicester with full PhD tuition fee scholarship. The authors thank to Dr Ovidiu Ivanciuc for organising the CoEPrA contest that provided the peptide binding affinity data sets. The authors also thank to Dr Ozgur-Demir Kavuk for his assistance in providing the binding affinities of the test data sets.

Funders	Funder number
De Montfort University

Keywords

Fuzzy systems
Peptide binding affinity
Support vector regression

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10.1016/j.asoc.2016.01.024

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title = "Quantitative prediction of peptide binding affinity by using hybrid fuzzy support vector regression",

keywords = "Fuzzy systems, Peptide binding affinity, Support vector regression",

author = "Volkan Uslan and Huseyin Seker",

note = "Publisher Copyright: {\textcopyright} 2016 The Authors. Published by Elsevier B.V.",

year = "2016",

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journal = "Applied Soft Computing",

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AU - Seker, Huseyin

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Quantitative prediction of peptide binding affinity by using hybrid fuzzy support vector regression

Funding

Keywords

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