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Accepted manuscript
ApicoAMP: The first computational model for identifying apicoplast-targeted transmembrane proteins in Apicomplexa
Journal of microbiological methods., Vol.95(3), pp.313-319
12/2013
Handle:
https://hdl.handle.net/2376/6117
Abstract
In this work, we develop a method for predicting apicoplast-targeted transmembrane proteins for multiple species of Apicomplexa, whereby several classifiers trained on different feature sets and based on different algorithms are evaluated and combined in an ensemble classification model to obtain the best expected performance. The feature sets considered are the hydrophobicity and composition characteristics of amino acids over transmembrane domains, the existence of short sequence motifs over cytosolically disposed regions, and Gene Ontology (GO) terms associated with given proteins. Our model, ApicoAMP, is an ensemble classification model that combines decisions of classifiers following the majority vote principle. ApicoAMP is trained on a set of proteins from 11 apicomplexan species and achieves 91% overall expected accuracy.
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Details
- Title
- ApicoAMP: The first computational model for identifying apicoplast-targeted transmembrane proteins in Apicomplexa
- Creators
- Gokcen Cilingir (Author)Audrey O.T. Lau (Author)Shira L. Broschat (Author)
- Publication Details
- Journal of microbiological methods., Vol.95(3), pp.313-319
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Publisher
- Elsevier
- Identifiers
- 99900502384701842
- Copyright
- Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International ; https://creativecommons.org/licenses/by-nc-nd/4.0/
- Language
- English
- Resource Type
- Accepted manuscript