Feature Selection and Ranking of Key Genes for Tumor Classification: Using Microarray Gene Expression Data

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Srinivas Mukkamala and Qingzhong Liu and 
                 Rajeev Veeraghattam and Andrew H. Sung",
  title =        "Feature Selection and Ranking of Key Genes for Tumor
                 Classification: Using Microarray Gene Expression Data",
  booktitle =    "Proceedings 8th International Conference on Artificial
                 Intelligence and Soft Computing {ICAISC}",
  year =         "2006",
  pages =        "951--961",
  series =       "Lecture Notes on Artificial Intelligence (LNAI)",
  volume =       "4029",
  publisher =    "Springer-Verlag",
  editor =       "Leszek Rutkowski and Ryszard Tadeusiewicz and 
                 Lotfi A. Zadeh and Jacek Zurada",
  address =      "Zakopane, Poland",
  month =        jun # " 25-29",
  keywords =     "genetic algorithms, genetic programming, ROC",
  ISBN =         "3-540-35748-3",
  DOI =          "doi:10.1007/11785231_100",
  size =         "11 pages",
  abstract =     "In this paper we perform a t-test for significant gene
                 expression analysis in different dimensions based on
                 molecular profiles from micro array data, and compare
                 several computational intelligent techniques for
                 classification accuracy on Leukemia, Lymphoma and
                 Prostate cancer datasets of broad institute and Colon
                 cancer dataset from Princeton gene expression project.
                 Classification accuracy is evaluated with Linear
                 genetic Programs, Multivariate Regression Splines
                 (MARS), Classification and Regression Tress (CART) and
                 Random Forests. Linear Genetic Programs and Random
                 forests perform the best for detecting malignancy of
                 different tumours. Our results demonstrate the
                 potential of using learning machines in diagnosis of
                 the malignancy of a tumour.

                 We also address the related issue of ranking the
                 importance of input features, which is itself a problem
                 of great interest. Elimination of the insignificant
                 inputs (genes) leads to a simplified problem and
                 possibly faster and more accurate classification of
                 microarray gene expression data. Experiments on select
                 cancer datasets have been carried out to assess the
                 effectiveness of this criterion. Results show that
                 using significant features gives the most remarkable
                 performance and performs consistently well over micro
                 array gene expression datasets we used. The classifiers
                 used perform the best using the most significant
                 features expect for Prostate cancer dataset.",

Genetic Programming entries for Srinivas Mukkamala Qingzhong Liu Rajeev Veeraghattam Andrew H Sung