Borschbach, M.; Hauke, S.; Pyka, M.; Heider, Dominik:
Opportunities and limitations of a principal component analysis optimized machine learning approach for the identification and classification of cancer involved proteins
2009
In: Communications of SIWN, Heft 6, S. 85 - 89
Artikel/Aufsatz in Zeitschrift / Fach: Biologie; Medizin; Informatik
Titel:
Opportunities and limitations of a principal component analysis optimized machine learning approach for the identification and classification of cancer involved proteins
Autor(in):
Borschbach, M.; Hauke, S.; Pyka, M.; Heider, Dominik im Online-Personal- und -Vorlesungsverzeichnis LSF anzeigen
Erscheinungsjahr:
2009
Erschienen in:
Communications of SIWN, Heft 6, S. 85 - 89
ISSN:

Abstract:

The computational reduction of the input dimension of different parts of the input layer for the prediction of functional protein classes is presented. The pro teins are preprocessed and two feed forward neural networks were trained with a dataset containing small GTPases and non~GTPases. The results of the principle component analysis (PCA) optimized neural network and the neural network without PCA were compared. The computational complexity and the prediction accuracy are key aspects of the discussion of the statistical verified simulation results. Moreover, the mathematical background of the PCA and therefore the influence of the PCAdimension on the prediction quality as an underlying optimization problem based on the necessary selection of eigenvectors of the feature space is considered.