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 Aurora harvest 8 |
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hdl_116072.pdf | Published version | 866.15 kB | Adobe PDF | View/Open |
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