Data mining methods for gene selection on the basis of gene expression arrays

• Autorzy: Michał Muszyński , Stanisław Osowski
• Języki publikacji: EN

Abstrakty

EN

The paper presents data mining methods applied to gene selection for recognition of a particular type of prostate cancer on the basis of gene expression arrays. Several chosen methods of gene selection, including the Fisher method, correlation of gene with a class, application of the support vector machine and statistical hypotheses, are compared on the basis of clustering measures. The results of applying these individual selection methods are combined together to identify the most often selected genes forming the required pattern, best associated with the cancerous cases. This resulting pattern of selected gene lists is treated as the input data to the classifier, performing the task of the final recognition of the patterns. The numerical results of the recognition of prostate cancer from normal (reference) cases using the selected genes and the support vector machine confirm the good performance of the proposed gene selection approach.

Słowa kluczowe

EN

gene expression array; gene ranking; feature selection; clusterization measures; fusion; SVM classification

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2014
• Tom: 24
• Numer: 3
• Strony: 657-668

Daty

wydano

2014

otrzymano

2013-09-17

poprawiono

2014-01-15

poprawiono

2014-03-09

Twórcy

autor

Michał Muszyński

• Faculty of Electrical Engineering, Warsaw University of Technology, pl. Politechniki 1, 00-661 Warsaw, Poland

autor

Stanisław Osowski

• Faculty of Electrical Engineering, Warsaw University of Technology, pl. Politechniki 1, 00-661 Warsaw, Poland
• Faculty of Electronic Engineering, Military University of Technology, ul. Kaliskiego 2, 00-908 Warsaw, Poland

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Identyfikatory

DOI

10.2478/amcs-2014-0048