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Proceedings Paper

Prediction of protein phosphorylation sites using classification trees and SVM classifier
Author(s): Piotr Betkier; Zbigniew Szymański
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Paper Abstract

The paper presents a method of solving the problem of protein phosphorylation sites recognition. Six classifiers were created for prediction whether specified amino acid sequences represented as a 9-character strings react with given types of the kinase-enzymes. The method consists of three steps. Positions in the amino acid sequences significant for classification are found with the use of classification trees in the first step. Afterwards, the symbols composing the sequences are mapped to the real numbers domain using the Gini index method. The last step consists of creating the SVM classifiers as the final prediction models. The paper contains evaluation of the obtained results and the description of the methods applied to evaluate the quality of the classifiers.

Paper Details

Date Published: 7 October 2011
PDF: 8 pages
Proc. SPIE 8008, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2011, 80081N (7 October 2011); doi: 10.1117/12.905778
Show Author Affiliations
Piotr Betkier, Warsaw Univ. of Technology (Poland)
Zbigniew Szymański, Warsaw Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 8008:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2011
Ryszard S. Romaniuk, Editor(s)

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