Biomarker Discovery Using 1-norm Regularization for Multiclass Earthworm Microarray Gene Expression Data

Authors

Xiaofei Nan, University of Mississippi
Nan Wang, University of Southern MississippiFollow
Ping Gong, SpecPro, Inc.Follow
Chaoyang Zhang, University of Southern MississippiFollow
Yixin Chen, University of MississippiFollow
Dawn Wilkins, University of MississippiFollow

Document Type

Article

Publication Date

9-1-2012

Department

Computing

School

Computing Sciences and Computer Engineering

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. (c) 2012 Elsevier B.V. All rights reserved.

Publication Title

Neurocomputing

Volume

92

First Page

36

Last Page

43

Recommended Citation

Nan, X., Wang, N., Gong, P., Zhang, C., Chen, Y., Wilkins, D. (2012). Biomarker Discovery Using 1-norm Regularization for Multiclass Earthworm Microarray Gene Expression Data. Neurocomputing, 92, 36-43.
Available at: https://aquila.usm.edu/fac_pubs/141