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

Michał Muszyński; Stanisław Osowski

International Journal of Applied Mathematics and Computer Science (2014)

  • Volume: 24, Issue: 3, page 657-668
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Michał Muszyński, and Stanisław Osowski. "Data mining methods for gene selection on the basis of gene expression arrays." International Journal of Applied Mathematics and Computer Science 24.3 (2014): 657-668. <http://eudml.org/doc/271873>.

@article{MichałMuszyński2014,
abstract = {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.},
author = {Michał Muszyński, Stanisław Osowski},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {gene expression array; gene ranking; feature selection; clusterization measures; fusion; SVM classification},
language = {eng},
number = {3},
pages = {657-668},
title = {Data mining methods for gene selection on the basis of gene expression arrays},
url = {http://eudml.org/doc/271873},
volume = {24},
year = {2014},
}

TY - JOUR
AU - Michał Muszyński
AU - Stanisław Osowski
TI - Data mining methods for gene selection on the basis of gene expression arrays
JO - International Journal of Applied Mathematics and Computer Science
PY - 2014
VL - 24
IS - 3
SP - 657
EP - 668
AB - 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.
LA - eng
KW - gene expression array; gene ranking; feature selection; clusterization measures; fusion; SVM classification
UR - http://eudml.org/doc/271873
ER -

References

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