A signature-based liver cancer predictive system

Ray R. Hashemi; Mahmood Bahar; Joshua H. Early; Alexander A. Tyler; John F. Young

doi:10.1109/itcc.2005.37

A signature-based liver cancer predictive system

Ray R. Hashemi
, Mahmood Bahar
, Joshua H. Early
, Alexander A. Tyler
, John F. Young

Computer Science

Armstrong Atlantic State University
Kharazmi University
University College Cork
United States Food and Drug Administration

Research output: Contribution to book or proceeding › Conference article › peer-review

2 Scopus citations

Abstract

The predictive system presented in this paper employs both SOM and Hopfield nets to determine whether a given chemical agent causes cancer in the liver. The SOM net performs the clustering of the training set and delivers a signature for each cluster. Hopfield net treats each signature as an exemplar and learns the exemplars. Each record of the test set is considered a corrupted signature. The Hopfield net tries to un-corrupt the test record using learned exemplars and map it to one of the signatures and consequently to the prediction value associated -with the signature. Four pairs of training and test sets are used to test the system. To establish the validity of the new predictive system, its performance is compared with the performance of the Discriminant analysis and the Rough Sets methodology applied on the same datasets.

Original language	English
Title of host publication	Proceedings ITCC 2005 - International Conference on Information Technology
Subtitle of host publication	Coding and Computing
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	195-199
Number of pages	5
ISBN (Print)	0769523153, 9780769523156
DOIs	https://doi.org/10.1109/itcc.2005.37
State	Published - 2005
Event	ITCC 2005 - International Conference on Information Technology: Coding and Computing - Las Vegas, NV, United States Duration: Apr 4 2005 → Apr 6 2005

Publication series

Name	International Conference on Information Technology: Coding and Computing, ITCC
Volume	1

Conference

Conference	ITCC 2005 - International Conference on Information Technology: Coding and Computing
Country/Territory	United States
City	Las Vegas, NV
Period	04/4/05 → 04/6/05

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Scopus Subject Areas

General Engineering

Keywords

And Hopfield Net
Carcinogenic Potency Database
Liver Cancer
Predictive Systems
Self-organizing Map (SOM)

Access to Document

10.1109/itcc.2005.37

Cite this

Hashemi, R. R., Bahar, M., Early, J. H., Tyler, A. A., & Young, J. F. (2005). A signature-based liver cancer predictive system. In Proceedings ITCC 2005 - International Conference on Information Technology: Coding and Computing (pp. 195-199). (International Conference on Information Technology: Coding and Computing, ITCC; Vol. 1). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/itcc.2005.37

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title = "A signature-based liver cancer predictive system",

abstract = "The predictive system presented in this paper employs both SOM and Hopfield nets to determine whether a given chemical agent causes cancer in the liver. The SOM net performs the clustering of the training set and delivers a signature for each cluster. Hopfield net treats each signature as an exemplar and learns the exemplars. Each record of the test set is considered a corrupted signature. The Hopfield net tries to un-corrupt the test record using learned exemplars and map it to one of the signatures and consequently to the prediction value associated -with the signature. Four pairs of training and test sets are used to test the system. To establish the validity of the new predictive system, its performance is compared with the performance of the Discriminant analysis and the Rough Sets methodology applied on the same datasets.",

keywords = "And Hopfield Net, Carcinogenic Potency Database, Liver Cancer, Predictive Systems, Self-organizing Map (SOM)",

author = "Hashemi, \{Ray R.\} and Mahmood Bahar and Early, \{Joshua H.\} and Tyler, \{Alexander A.\} and Young, \{John F.\}",

year = "2005",

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language = "English",

isbn = "0769523153",

series = "International Conference on Information Technology: Coding and Computing, ITCC",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "195--199",

booktitle = "Proceedings ITCC 2005 - International Conference on Information Technology",

address = "United States",

note = "ITCC 2005 - International Conference on Information Technology: Coding and Computing ; Conference date: 04-04-2005 Through 06-04-2005",

}

Hashemi, RR, Bahar, M, Early, JH, Tyler, AA & Young, JF 2005, A signature-based liver cancer predictive system. in Proceedings ITCC 2005 - International Conference on Information Technology: Coding and Computing. International Conference on Information Technology: Coding and Computing, ITCC, vol. 1, Institute of Electrical and Electronics Engineers Inc., pp. 195-199, ITCC 2005 - International Conference on Information Technology: Coding and Computing, Las Vegas, NV, United States, 04/4/05. https://doi.org/10.1109/itcc.2005.37

A signature-based liver cancer predictive system. / Hashemi, Ray R.; Bahar, Mahmood; Early, Joshua H. et al.
Proceedings ITCC 2005 - International Conference on Information Technology: Coding and Computing. Institute of Electrical and Electronics Engineers Inc., 2005. p. 195-199 (International Conference on Information Technology: Coding and Computing, ITCC; Vol. 1).

Research output: Contribution to book or proceeding › Conference article › peer-review

TY - GEN

T1 - A signature-based liver cancer predictive system

AU - Hashemi, Ray R.

AU - Bahar, Mahmood

AU - Early, Joshua H.

AU - Tyler, Alexander A.

AU - Young, John F.

PY - 2005

Y1 - 2005

N2 - The predictive system presented in this paper employs both SOM and Hopfield nets to determine whether a given chemical agent causes cancer in the liver. The SOM net performs the clustering of the training set and delivers a signature for each cluster. Hopfield net treats each signature as an exemplar and learns the exemplars. Each record of the test set is considered a corrupted signature. The Hopfield net tries to un-corrupt the test record using learned exemplars and map it to one of the signatures and consequently to the prediction value associated -with the signature. Four pairs of training and test sets are used to test the system. To establish the validity of the new predictive system, its performance is compared with the performance of the Discriminant analysis and the Rough Sets methodology applied on the same datasets.

AB - The predictive system presented in this paper employs both SOM and Hopfield nets to determine whether a given chemical agent causes cancer in the liver. The SOM net performs the clustering of the training set and delivers a signature for each cluster. Hopfield net treats each signature as an exemplar and learns the exemplars. Each record of the test set is considered a corrupted signature. The Hopfield net tries to un-corrupt the test record using learned exemplars and map it to one of the signatures and consequently to the prediction value associated -with the signature. Four pairs of training and test sets are used to test the system. To establish the validity of the new predictive system, its performance is compared with the performance of the Discriminant analysis and the Rough Sets methodology applied on the same datasets.

KW - And Hopfield Net

KW - Carcinogenic Potency Database

KW - Liver Cancer

KW - Predictive Systems

KW - Self-organizing Map (SOM)

UR - https://www.scopus.com/pages/publications/24744438695

U2 - 10.1109/itcc.2005.37

DO - 10.1109/itcc.2005.37

M3 - Conference article

SN - 0769523153

SN - 9780769523156

T3 - International Conference on Information Technology: Coding and Computing, ITCC

SP - 195

EP - 199

BT - Proceedings ITCC 2005 - International Conference on Information Technology

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - ITCC 2005 - International Conference on Information Technology: Coding and Computing

Y2 - 4 April 2005 through 6 April 2005

ER -

Hashemi RR, Bahar M, Early JH, Tyler AA, Young JF. A signature-based liver cancer predictive system. In Proceedings ITCC 2005 - International Conference on Information Technology: Coding and Computing. Institute of Electrical and Electronics Engineers Inc. 2005. p. 195-199. (International Conference on Information Technology: Coding and Computing, ITCC). doi: 10.1109/itcc.2005.37

A signature-based liver cancer predictive system

Abstract

Publication series

Conference

UN SDGs

Scopus Subject Areas

Keywords

Access to Document

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