Table of Contents
Computational Biology Journal
Volume 2014, Article ID 418069, 5 pages
http://dx.doi.org/10.1155/2014/418069
Research Article

Restricted Boltzmann Machines for Classification of Hepatocellular Carcinoma

1Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA
2Department of Biological Sciences, University of Texas at El Paso, El Paso, TX 79968, USA

Received 21 November 2013; Revised 14 March 2014; Accepted 28 March 2014; Published 14 April 2014

Academic Editor: Rosalia Maglietta

Copyright © 2014 James A. Koziol et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Multiple antigen miniarrays can provide accurate tools for cancer detection and diagnosis. These miniarrays can be validated by examining their operating characteristics in classifying individuals as either cancer patients or normal (non-cancer) subjects. We describe the use of restricted Boltzmann machines for this classification problem, relative to diagnosis of hepatocellular carcinoma. In this setting, we find that its operating characteristics are similar to a logistic regression standard and suggest that restricted Boltzmann machines merit further consideration for classification problems.