Abstract

To accurately identify the site of origin of a tumor is crucial to cancer diagnosis and treatment. With the emergence of DNA microarray technologies, constructing gene expression profiles for different cancer types has already become a promising means for cancer classification. In addition to binary classification, the discrimination of multiple tumor types is also important semi-supervised ellipsoid ARTMAP (ssEAM) is a novel neural network architecture rooted in adaptive resonance theory suitable for classification tasks. ssEAM can achieve fast, stable and finite learning and create hyper-ellipsoidal clusters inducing complex nonlinear decision boundaries. Here, we demonstrate the capability of ssEAM to discriminate multi-class cancer through analyzing two publicly available cancer datasets based on their gene expression profiles.

Meeting Name

26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Sponsor(s)

Mary K. Finley Missouri Endowment
National Science Foundation (U.S.)

Keywords and Phrases

ART Neural Nets; Cancer; Genetics; Medical Diagnostic Computing; Molecular Biophysics; Neural Net Architecture; Patient Diagnosis; Tumours

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2004 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Full Text Link

Share

 
COinS