Hybrid intelligent methods for microarray data analysis

Ganeshkumar Pugalendhi; Ku Jin Kim

doi:10.1109/BIBE.2015.7367704

Hybrid intelligent methods for microarray data analysis

Ganeshkumar Pugalendhi
, Ku Jin Kim

School of Computer Science & Engineering

Kyungpook National University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Data produced out of microarray experiments are of great use for the physician when it is presented in a meaningful manner. This paper proposes hybrid intelligent methods for addressing the challenges in analyzing the microarray data. The concept of fuzzy and rough set is hybridized with FInformation (FRFI) for gene selection. An optimal fuzzy logic based classifier (FLC) is developed for sample classification using a hybrid Genetic Swarm Algorithm (GSA). Detailed experiments are conducted using microarray data related to Cancer and Rheumatoid Arthritis. From the simulation study, it is found that the proposed FRFI-FLC-GSA produces compact classification system with reasonably good informative genes that can be used for disease diagnosis.

Original language	English
Title of host publication	2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467379830
DOIs	https://doi.org/10.1109/BIBE.2015.7367704
State	Published - 28 Dec 2015
Event	15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015 - Belgrade, Serbia Duration: 2 Nov 2015 → 4 Nov 2015

Publication series

Name	2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015

Conference

Conference	15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015
Country/Territory	Serbia
City	Belgrade
Period	2/11/15 → 4/11/15

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

FInformation
Fuzzy-Rough Set
Genetic Swarm Algorithm
Microarray data

Access to Document

10.1109/BIBE.2015.7367704

Cite this

Pugalendhi, G., & Kim, K. J. (2015). Hybrid intelligent methods for microarray data analysis. In 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015 Article 7367704 (2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBE.2015.7367704

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title = "Hybrid intelligent methods for microarray data analysis",

abstract = "Data produced out of microarray experiments are of great use for the physician when it is presented in a meaningful manner. This paper proposes hybrid intelligent methods for addressing the challenges in analyzing the microarray data. The concept of fuzzy and rough set is hybridized with FInformation (FRFI) for gene selection. An optimal fuzzy logic based classifier (FLC) is developed for sample classification using a hybrid Genetic Swarm Algorithm (GSA). Detailed experiments are conducted using microarray data related to Cancer and Rheumatoid Arthritis. From the simulation study, it is found that the proposed FRFI-FLC-GSA produces compact classification system with reasonably good informative genes that can be used for disease diagnosis.",

keywords = "FInformation, Fuzzy-Rough Set, Genetic Swarm Algorithm, Microarray data",

author = "Ganeshkumar Pugalendhi and Kim, \{Ku Jin\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015 ; Conference date: 02-11-2015 Through 04-11-2015",

year = "2015",

month = dec,

day = "28",

doi = "10.1109/BIBE.2015.7367704",

language = "English",

series = "2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015",

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

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}

Pugalendhi, G & Kim, KJ 2015, Hybrid intelligent methods for microarray data analysis. in 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015., 7367704, 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015, Institute of Electrical and Electronics Engineers Inc., 15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015, Belgrade, Serbia, 2/11/15. https://doi.org/10.1109/BIBE.2015.7367704

Hybrid intelligent methods for microarray data analysis. / Pugalendhi, Ganeshkumar; Kim, Ku Jin.
2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7367704 (2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Hybrid intelligent methods for microarray data analysis

AU - Pugalendhi, Ganeshkumar

AU - Kim, Ku Jin

PY - 2015/12/28

Y1 - 2015/12/28

N2 - Data produced out of microarray experiments are of great use for the physician when it is presented in a meaningful manner. This paper proposes hybrid intelligent methods for addressing the challenges in analyzing the microarray data. The concept of fuzzy and rough set is hybridized with FInformation (FRFI) for gene selection. An optimal fuzzy logic based classifier (FLC) is developed for sample classification using a hybrid Genetic Swarm Algorithm (GSA). Detailed experiments are conducted using microarray data related to Cancer and Rheumatoid Arthritis. From the simulation study, it is found that the proposed FRFI-FLC-GSA produces compact classification system with reasonably good informative genes that can be used for disease diagnosis.

AB - Data produced out of microarray experiments are of great use for the physician when it is presented in a meaningful manner. This paper proposes hybrid intelligent methods for addressing the challenges in analyzing the microarray data. The concept of fuzzy and rough set is hybridized with FInformation (FRFI) for gene selection. An optimal fuzzy logic based classifier (FLC) is developed for sample classification using a hybrid Genetic Swarm Algorithm (GSA). Detailed experiments are conducted using microarray data related to Cancer and Rheumatoid Arthritis. From the simulation study, it is found that the proposed FRFI-FLC-GSA produces compact classification system with reasonably good informative genes that can be used for disease diagnosis.

KW - FInformation

KW - Fuzzy-Rough Set

KW - Genetic Swarm Algorithm

KW - Microarray data

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DO - 10.1109/BIBE.2015.7367704

M3 - Conference contribution

AN - SCOPUS:84962858108

T3 - 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015

BT - 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015

Y2 - 2 November 2015 through 4 November 2015

ER -

Hybrid intelligent methods for microarray data analysis

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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