Extreme Value Distribution Based Gene Selection Criteria for Discriminant Microarray Data Analysis Using Logistic Regression

@article{Li2004ExtremeVD,
  title={Extreme Value Distribution Based Gene Selection Criteria for Discriminant Microarray Data Analysis Using Logistic Regression},
  author={Wentian Li and Fengzhu Sun and Ivo Grosse},
  journal={Journal of computational biology : a journal of computational molecular cell biology},
  year={2004},
  volume={11 2-3},
  pages={215-26}
}
One important issue commonly encountered in the analysis of microarray data is to decide which and how many genes should be selected for further studies. For discriminant microarray data analyses based on statistical models, such as the logistic regression models, gene selection can be accomplished by a comparison of the maximum likelihood of the model given the real data, L(D|M), and the expected maximum likelihood of the model given an ensemble of surrogate data with randomly permuted label… CONTINUE READING