Applying Fuzzy ART in medical diagnosis of cancers

Jen Ing G Hwang, Chih En Liu, Lori J Sokoll, Bao Ling Adam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many researchers have used proteomic mass spectrometry for cancer detection and with various types of data preprocessing and classification methods to overcome data complexities. This research focuses on discovering different cancers display their unique proteomic patterns or have similar patterns with others. To meet this goal, this study introduces a complete data preprocessing procedure and applies a Fuzzy ART clustering method to differentiate the patterns among multiple cancer diseases. This approach shows the potential of separating cancer patterns from healthy patterns, as well as among different types of cancers.

Original languageEnglish (US)
Title of host publication2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Pages1084-1089
Number of pages6
Volume3
DOIs
StatePublished - 2010
Event2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China
Duration: Jul 11 2010Jul 14 2010

Other

Other2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
CountryChina
CityQingdao
Period7/11/107/14/10

Fingerprint

Fuzzy clustering
Mass spectrometry
Proteomics

Keywords

  • Cancer detection
  • Fuzzy ART
  • Pattern recognition
  • Proteomics
  • Recursive-SVM

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Human-Computer Interaction

Cite this

Hwang, J. I. G., Liu, C. E., Sokoll, L. J., & Adam, B. L. (2010). Applying Fuzzy ART in medical diagnosis of cancers. In 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 (Vol. 3, pp. 1084-1089). [5580939] https://doi.org/10.1109/ICMLC.2010.5580939

Applying Fuzzy ART in medical diagnosis of cancers. / Hwang, Jen Ing G; Liu, Chih En; Sokoll, Lori J; Adam, Bao Ling.

2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. Vol. 3 2010. p. 1084-1089 5580939.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hwang, JIG, Liu, CE, Sokoll, LJ & Adam, BL 2010, Applying Fuzzy ART in medical diagnosis of cancers. in 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. vol. 3, 5580939, pp. 1084-1089, 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, Qingdao, China, 7/11/10. https://doi.org/10.1109/ICMLC.2010.5580939
Hwang JIG, Liu CE, Sokoll LJ, Adam BL. Applying Fuzzy ART in medical diagnosis of cancers. In 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. Vol. 3. 2010. p. 1084-1089. 5580939 https://doi.org/10.1109/ICMLC.2010.5580939
Hwang, Jen Ing G ; Liu, Chih En ; Sokoll, Lori J ; Adam, Bao Ling. / Applying Fuzzy ART in medical diagnosis of cancers. 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. Vol. 3 2010. pp. 1084-1089
@inproceedings{109b47d8723a487ea9d415353eee0bb9,
title = "Applying Fuzzy ART in medical diagnosis of cancers",
abstract = "Many researchers have used proteomic mass spectrometry for cancer detection and with various types of data preprocessing and classification methods to overcome data complexities. This research focuses on discovering different cancers display their unique proteomic patterns or have similar patterns with others. To meet this goal, this study introduces a complete data preprocessing procedure and applies a Fuzzy ART clustering method to differentiate the patterns among multiple cancer diseases. This approach shows the potential of separating cancer patterns from healthy patterns, as well as among different types of cancers.",
keywords = "Cancer detection, Fuzzy ART, Pattern recognition, Proteomics, Recursive-SVM",
author = "Hwang, {Jen Ing G} and Liu, {Chih En} and Sokoll, {Lori J} and Adam, {Bao Ling}",
year = "2010",
doi = "10.1109/ICMLC.2010.5580939",
language = "English (US)",
isbn = "9781424465262",
volume = "3",
pages = "1084--1089",
booktitle = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010",

}

TY - GEN

T1 - Applying Fuzzy ART in medical diagnosis of cancers

AU - Hwang, Jen Ing G

AU - Liu, Chih En

AU - Sokoll, Lori J

AU - Adam, Bao Ling

PY - 2010

Y1 - 2010

N2 - Many researchers have used proteomic mass spectrometry for cancer detection and with various types of data preprocessing and classification methods to overcome data complexities. This research focuses on discovering different cancers display their unique proteomic patterns or have similar patterns with others. To meet this goal, this study introduces a complete data preprocessing procedure and applies a Fuzzy ART clustering method to differentiate the patterns among multiple cancer diseases. This approach shows the potential of separating cancer patterns from healthy patterns, as well as among different types of cancers.

AB - Many researchers have used proteomic mass spectrometry for cancer detection and with various types of data preprocessing and classification methods to overcome data complexities. This research focuses on discovering different cancers display their unique proteomic patterns or have similar patterns with others. To meet this goal, this study introduces a complete data preprocessing procedure and applies a Fuzzy ART clustering method to differentiate the patterns among multiple cancer diseases. This approach shows the potential of separating cancer patterns from healthy patterns, as well as among different types of cancers.

KW - Cancer detection

KW - Fuzzy ART

KW - Pattern recognition

KW - Proteomics

KW - Recursive-SVM

UR - http://www.scopus.com/inward/record.url?scp=78149355244&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78149355244&partnerID=8YFLogxK

U2 - 10.1109/ICMLC.2010.5580939

DO - 10.1109/ICMLC.2010.5580939

M3 - Conference contribution

AN - SCOPUS:78149355244

SN - 9781424465262

VL - 3

SP - 1084

EP - 1089

BT - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

ER -