Multi-class Cancer Classification by Semi-Supervised Ellipsoid ARTMAP with Gene Expression Data

Rui Xu; Georgios C. Anagnostopoulos; Donald C. Wunsch

doi:10.1109/IEMBS.2004.1403123

Multi-class Cancer Classification by Semi-Supervised Ellipsoid ARTMAP with Gene Expression Data

Rui Xu
, Georgios C. Anagnostopoulos
, Donald C. Wunsch

Missouri University of Science and Technology
Florida Institute of Technology

Research output: Contribution to journal › Article › peer-review

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.

Original language	American English
Pages (from-to)	188-191
Number of pages	4
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	26 I
DOIs	https://doi.org/10.1109/IEMBS.2004.1403123
State	Published - Sep 1 2004
Externally published	Yes
Event	Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States Duration: Sep 1 2004 → Sep 5 2004

ASJC Scopus Subject Areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Keywords

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

Disciplines

Electrical and Computer Engineering

Access to Document

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Multi-class Cancer Classification by Semi-Supervised Ellipsoid ARFinal published version, 378 KBLicense: Unspecified

Cite this

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title = "Multi-class Cancer Classification by Semi-Supervised Ellipsoid ARTMAP with Gene Expression Data",

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.",

keywords = "ART Neural Nets, Cancer, Genetics, Medical Diagnostic Computing, Molecular Biophysics, Neural Net Architecture, Patient Diagnosis, Tumours",

author = "Rui Xu and Anagnostopoulos, \{Georgios C.\} and Wunsch, \{Donald C.\}",

year = "2004",

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doi = "10.1109/IEMBS.2004.1403123",

language = "American English",

volume = "26 I",

pages = "188--191",

journal = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",

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note = "Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 ; Conference date: 01-09-2004 Through 05-09-2004",

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AU - Xu, Rui

AU - Anagnostopoulos, Georgios C.

AU - Wunsch, Donald C.

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Y1 - 2004/9/1

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AB - 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.

KW - ART Neural Nets

KW - Cancer

KW - Genetics

KW - Medical Diagnostic Computing

KW - Molecular Biophysics

KW - Neural Net Architecture

KW - Patient Diagnosis

KW - Tumours

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U2 - 10.1109/IEMBS.2004.1403123

DO - 10.1109/IEMBS.2004.1403123

M3 - Article

SN - 0589-1019

VL - 26 I

SP - 188

EP - 191

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

T2 - Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004

Y2 - 1 September 2004 through 5 September 2004

ER -

Multi-class Cancer Classification by Semi-Supervised Ellipsoid ARTMAP with Gene Expression Data

Abstract

ASJC Scopus Subject Areas

Keywords

Disciplines

Access to Document

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