Selection of informative genes in gene expression based diagnosis: A nonparametric approach

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1933, Issue , 2000, Pages 300-307

  Beibel, Martin ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BIOCHIPS; DATA HANDLING; GENE EXPRESSION; INFORMATION ANALYSIS;

CANCER CLASSIFICATION; DNA MICRO-ARRAY; INFORMATIVE GENES; NONPARAMETRIC APPROACHES;

DIAGNOSIS;

EID: 84954431348     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-39949-6_36     Document Type: Conference Paper

References (6)

1. Alizadeh, A. A. et al: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403 (2000) 503-511
2. Ciampi, A. et al: Recursive partioning: a versatile method for exploratory data analysis in Biostatistics, in Biostatistics (eds. I. B. MacNeill and G. J. Umphrey). (1987) D. Reidel Publishing, New York
3. Gail, M. H. and Green, S. B.: A generalization of the one-sided two-sample Kolmogorov-Smirnov statistic for evaluating diagnostic tests. Biometrics 32 (1976) 561-570
4. Golub, T. R. et al: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531-536
5. Miller, R. and Siegmund, D.: Maximally selected chi-square statistics. Biometrics 38 (1982) 1011-1016
6. Slonim, D. K. et al: Class prediction and discovery using gene expression data. Manuscript available at http://www.genome.wi.mit.edu/HPR