Work

Restricted Boltzmann Machines for Classification of Hepatocellular Carcinoma

Year: 2014

Type: article

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.

Source: Computational Biology Journal

DOI: 10.1155/2014/418069

Authors: James A. Koziol, Eng M. Tan, Liping Dai, Pengfei Ren, Jian-Ying Zhang

Institutions: Scripps Research Institute, The University of Texas at El Paso

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funder: National Institutes of Health