Biomarker discovery using 1-norm regularization for multiclass earthworm microarray gene expression data

Authors:
Xiaofei Nan;Nan Wang;Ping Gong;Chaoyang Zhang;Yixin Chen;Dawn Wilkins
Affiliations:
Department of Computer and Information Science, University of Mississippi, University, MS 38677, USA;School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA;SpecPro Inc., Environmental Services, Vicksburg, MS 39180, USA;School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA;Department of Computer and Information Science, University of Mississippi, University, MS 38677, USA;Department of Computer and Information Science, University of Mississippi, University, MS 38677, USA
Venue:
Neurocomputing
Year:
2012

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Abstract

Novel biomarkers can be discovered through mining high dimensional microarray datasets using machine learning techniques. Here we propose a novel recursive gene selection method which can handle the multiclass setting effectively and efficiently. The selection is performed iteratively. In each iteration, a linear multiclass classifier is trained using 1-norm regularization, which leads to sparse weight vectors, i.e., many feature weights are exactly zero. Those zero-weight features are eliminated in the next iteration. The empirical results demonstrate that the selected features (genes) have very competitive discriminative power. In addition, the selection process has fast rate of convergence.