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dc.creatorCilingir, Gokcen
dc.creatorLau, Audrey O.T.
dc.creatorBroschat, Shira L.
dc.description.abstractIn 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.en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subjectApicoplast-targeted membrane proteins
dc.subjectTransmembrane proteins
dc.subjectMachine learning
dc.subjectEnsemble classification models
dc.titleApicoAMP: The first computational model for identifying apicoplast-targeted transmembrane proteins in Apicomplexa
dc.description.citationCilingir, G., A.O.T. Lau, and S. L. Broschat, ApicoAMP: The first computational model for identifying apicoplast-targeted transmembrane proteins in Apicomplexa, Journal of Microbiological Methods, Vol. 95, No. 3, pp. 313-319, Dec. 2013. doi:10.1016/j.mimet.2013.09.017.

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  • Broschat, Shira
    This collection features research and educational materials by Shira Broschat, Professor and Curriculum Coordinator for the School of Electrical Engineering and Computer Science at Washington State University.

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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International