Extreme Value Distribution Based Gene Selection Criteria for Discriminant Microarray Data Analysis Using Logistic Regression-Reference-Cited by-同舟云学术

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

Published:2004-03 Issue:2-3 Volume:11 Page:215-226
ISSN:1066-5277
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Li Wentian¹,Sun Fengzhu²,Grosse Ivo³

Affiliation:

1. The Robert S. Boas Center for Genomics and Human Genetics, North Shore LIJ Research Institute, 350 Community Drive, Manhasset, NY 11030

2. Department of Biological Sciences, Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA 90089

3. Institute for Plant Genetics and Crop Plant Research, Corrensstrasse 3, D-06466 Gatersleben, Germany

Publisher

Mary Ann Liebert Inc

Subject

Computational Theory and Mathematics,Computational Mathematics,Genetics,Molecular Biology,Modeling and Simulation

Link

http://www.liebertpub.com/doi/pdf/10.1089/1066527041410445

Reference34 articles.

1. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays

2. Effective dimension reduction methods for tumor classification using gene expression data

3. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes

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3. Gene expression based cancer classification;Egyptian Informatics Journal;2017-11

4. Integrated Use of Statistical-Based Approaches and Computational Intelligence Techniques for Tumors Classification Using Microarray;Discrete Dynamics in Nature and Society;2015

5. Variable selection for Fisher linear discriminant analysis using the modified sequential backward selection algorithm for the microarray data;Applied Mathematics and Computation;2014-07

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