Prediction of protein phosphorylation sites using classification trees and SVM classifier

Details Prediction of protein phosphorylation sites using classification trees and SVM classifier Prediction of protein phosphorylation sites using classification trees and SVM classifier

Jun 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011spie.8008e..57b&link_type=abstract

Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2011. Edited by Romaniuk, Ry

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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.

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