Classifying subtypes of acute lymphoblastic leukemia using silhouette statistics and genetic algorithms.

[1]  Ru-Sheng Liu,et al.  Pattern classification in DNA microarray data of multiple tumor types , 2006, Pattern Recognit..

[2]  Xuefeng Bruce Ling,et al.  Multiclass cancer classification and biomarker discovery using GA-based algorithms , 2005, Bioinform..

[3]  Tao Li,et al.  A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression , 2004, Bioinform..

[4]  J. M. Deutsch,et al.  Evolutionary algorithms for finding optimal gene sets in microarray prediction , 2003, Bioinform..

[5]  Patrick Tan,et al.  Genetic algorithms applied to multi-class prediction for the analysis of gene expression data , 2003, Bioinform..

[6]  J. Downing,et al.  Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. , 2002, Cancer cell.

[7]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[8]  Thomas A. Darden,et al.  Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method , 2001, Bioinform..

[9]  M. Ringnér,et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.

[10]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[11]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[12]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[13]  P. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .