Robust biomarker identification for cancer diagnosis with ensemble feature selection methods

MOTIVATION Biomarker discovery is an important topic in biomedical applications of computational biology, including applications such as gene and SNP selection from high-dimensional data. Surprisingly, the stability with respect to sampling variation or robustness of such selection processes has received attention only recently. However, robustness of biomarkers is an important issue, as it may greatly influence subsequent biological validations. In addition, a more robust set of markers may strengthen the confidence of an expert in the results of a selection method. RESULTS Our first contribution is a general framework for the analysis of the robustness of a biomarker selection algorithm. Secondly, we conducted a large-scale analysis of the recently introduced concept of ensemble feature selection, where multiple feature selections are combined in order to increase the robustness of the final set of selected features. We focus on selection methods that are embedded in the estimation of support vector machines (SVMs). SVMs are powerful classification models that have shown state-of-the-art performance on several diagnosis and prognosis tasks on biological data. Their feature selection extensions also offered good results for gene selection tasks. We show that the robustness of SVMs for biomarker discovery can be substantially increased by using ensemble feature selection techniques, while at the same time improving upon classification performances. The proposed methodology is evaluated on four microarray datasets showing increases of up to almost 30% in robustness of the selected biomarkers, along with an improvement of approximately 15% in classification performance. The stability improvement with ensemble methods is particularly noticeable for small signature sizes (a few tens of genes), which is most relevant for the design of a diagnosis or prognosis model from a gene signature. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

[1]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[2]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[3]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[4]  Philip M. Long,et al.  Boosting and Microarray Data , 2003, Machine Learning.

[5]  Bernhard Schölkopf,et al.  Kernel Methods in Computational Biology , 2005 .

[6]  Lawrence Carin,et al.  Gene expression analysis: Joint feature selection and classifler design , 2004 .

[7]  Gunnar Rätsch,et al.  Support Vector Machines and Kernels for Computational Biology , 2008, PLoS Comput. Biol..

[8]  Yvan Saeys,et al.  Robust Feature Selection Using Ensemble Feature Selection Techniques , 2008, ECML/PKDD.

[9]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Geoffrey J McLachlan,et al.  Selection bias in gene extraction on the basis of microarray gene-expression data , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Tom Fawcett,et al.  Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions , 1997, KDD.

[12]  Thibault Helleputte,et al.  Feature Selection by Transfer Learning with Linear Regularized Models , 2009, ECML/PKDD.

[13]  Ludmila I. Kuncheva,et al.  A stability index for feature selection , 2007, Artificial Intelligence and Applications.

[14]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[15]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[16]  Thibault Helleputte,et al.  Partially supervised feature selection with regularized linear models , 2009, ICML '09.

[17]  Marcel Dettling,et al.  BagBoosting for tumor classification with gene expression data , 2004, Bioinform..

[18]  E. Lander,et al.  Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.

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

[20]  L. Carin,et al.  Gene expression analysis : Joint feature selection and classifier design , 2004 .

[21]  Edward R. Dougherty,et al.  Is cross-validation valid for small-sample microarray classification? , 2004, Bioinform..

[22]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[23]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[24]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[25]  Melanie Hilario,et al.  Knowledge and Information Systems , 2007 .

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

[27]  LarrañagaPedro,et al.  A review of feature selection techniques in bioinformatics , 2007 .