Classification methods for the development of genomic signatures from high-dimensional data

Personalized medicine is defined by the use of genomic signatures of patients to assign effective therapies. We present Classification by Ensembles from Random Partitions (CERP) for class prediction and apply CERP to genomic data on leukemia patients and to genomic data with several clinical variables on breast cancer patients. CERP performs consistently well compared to the other classification algorithms. The predictive accuracy can be improved by adding some relevant clinical/histopathological measurements to the genomic data.

[1]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[2]  Robert P. W. Duin,et al.  Limits on the majority vote accuracy in classifier fusion , 2003, Pattern Analysis & Applications.

[3]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[4]  R. Schapire.  The Strength of Weak Learnability , 1990, Machine Learning.

[5]  D. A. Williams,et al.  The analysis of binary responses from toxicological experiments involving reproduction and teratogenicity. , 1975, Biometrics.

[6]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[7]  Mike Clarke,et al.  Polychemotherapy for early breast cancer: an overview of the randomised trials , 1998, The Lancet.

[8]  Shili Lin,et al.  Class discovery and classification of tumor samples using mixture modeling of gene expression data - a unified approach , 2004, Bioinform..

[9]  James J. Chen,et al.  Classification by ensembles from random partitions of high-dimensional data , 2007, Comput. Stat. Data Anal..

[10]  Annette M. Molinaro,et al.  Prediction error estimation: a comparison of resampling methods , 2005, Bioinform..

[11]  Williams Da,et al.  The analysis of binary responses from toxicological experiments involving reproduction and teratogenicity. , 1975 .

[12]  W. L. McGuire,et al.  Breast cancer prognostic factors: evaluation guidelines. , 1991, Journal of the National Cancer Institute.

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

[14]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[15]  Yudong D. He,et al.  A Gene-Expression Signature as a Predictor of Survival in Breast Cancer , 2002 .

[16]  Hongshik Ahn,et al.  Generation of Over-Dispersed and Under-Dispersed Binomial Variates , 1995 .

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

[18]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[20]  Hyunjoong Kim,et al.  Classification Trees With Unbiased Multiway Splits , 2001 .

[21]  W. Loh,et al.  SPLIT SELECTION METHODS FOR CLASSIFICATION TREES , 1997 .

[22]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[23]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[24]  M. Xiong,et al.  Recursive partitioning for tumor classification with gene expression microarray data , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[26]  H. Ahn,et al.  Tree-structured logistic models for over-dispersed binomial data with application to modeling developmental effects. , 1997, Biometrics.