Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/116072
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Type: Conference paper
Title: Population entropies estimates of proteins
Author: Low, W.
Citation: AIP Conference Proceedings, 2017 / AbuBakar, S.A., Yunus, R.M., Mohamed, I. (ed./s), vol.1842, iss.1, pp.1-7
Publisher: American Institute of Physics
Issue Date: 2017
Series/Report no.: AIP Conference Proceedings
ISBN: 9780735415126
ISSN: 0094-243X
1551-7616
Conference Name: 3rd ISM International Statistical Conference 2016 (ISM-III) (9 Aug 2016 - 11 Aug 2016 : Kuala Lumpur, Malaysia)
Editor: AbuBakar, S.A.
Yunus, R.M.
Mohamed, I.
Statement of
Responsibility: 
Wai Yee Low
Abstract: The Shannon entropy equation provides a way to estimate variability of amino acids sequences in a multiple sequence alignment of proteins. Knowledge of protein variability is useful in many areas such as vaccine design, identification of antibody binding sites, and exploration of protein 3D structural properties. In cases where the population entropies of a protein are of interest but only a small sample size can be obtained, a method based on linear regression and random subsampling can be used to estimate the population entropy. This method is useful for comparisons of entropies where the actual sequence counts differ and thus, correction for alignment size bias is needed. In the current work, an R based package named EntropyCorrect that enables estimation of population entropy is presented and an empirical study on how well this new algorithm performs on simulated dataset of various combinations of population and sample sizes is discussed. The package is available at https://github.com/lloydlow/EntropyCorrect
Rights: © The Authors
DOI: 10.1063/1.4982834
Published version: http://dx.doi.org/10.1063/1.4982834
Appears in Collections:Animal and Veterinary Sciences publications
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