A fuzzy clustering algorithm for analysis of gene expression profiles

Han Saem Park, Si Ho Yoo, Sung Bae Cho

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

Advancement of DNA microarray technology has made it possible to get a great deal of biological information by a single experiment. Clustering algorithm is to group genes and reveal their functions or analyze unknown genes, which is categorized into hard and fuzzy clustering. For analyzing DNA microarray, fuzzy clustering can be better since genes can have several genetic information. In this paper, we present the GG (Gath-Geva) algorithm, which is one fuzzy clustering method, for clustering gene expression data. The GG algorithm is an improved version of the fuzzy c-means and GK (Gustafson-Kessel) algorithms and is appropriate for clustering gene expression data that have high dimension and ambiguous distribution. We have clustered serum and yeast data by the GG algorithm and compared it with the fuzzy c-means and GK algorithms. Through these experiments, we confirm that the GG algorithm is better for clustering gene expression data than other two algorithms.

Original languageEnglish
Pages (from-to)967-968
Number of pages2
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume3157
Publication statusPublished - 2004 Dec 1

Fingerprint

Gene Expression Profile
Fuzzy Algorithm
Fuzzy clustering
Fuzzy Clustering
Gene expression
Clustering algorithms
Clustering Algorithm
Gene Expression Data
DNA Microarray
Fuzzy C-means
Genes
Clustering
Microarrays
Gene
DNA
Ambiguous
Clustering Methods
Yeast
Higher Dimensions
Experiment

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Science(all)
  • Theoretical Computer Science

Cite this

@article{908b1934e8fa4ad993a9f0a6dc7d864d,
title = "A fuzzy clustering algorithm for analysis of gene expression profiles",
abstract = "Advancement of DNA microarray technology has made it possible to get a great deal of biological information by a single experiment. Clustering algorithm is to group genes and reveal their functions or analyze unknown genes, which is categorized into hard and fuzzy clustering. For analyzing DNA microarray, fuzzy clustering can be better since genes can have several genetic information. In this paper, we present the GG (Gath-Geva) algorithm, which is one fuzzy clustering method, for clustering gene expression data. The GG algorithm is an improved version of the fuzzy c-means and GK (Gustafson-Kessel) algorithms and is appropriate for clustering gene expression data that have high dimension and ambiguous distribution. We have clustered serum and yeast data by the GG algorithm and compared it with the fuzzy c-means and GK algorithms. Through these experiments, we confirm that the GG algorithm is better for clustering gene expression data than other two algorithms.",
author = "Park, {Han Saem} and Yoo, {Si Ho} and Cho, {Sung Bae}",
year = "2004",
month = "12",
day = "1",
language = "English",
volume = "3157",
pages = "967--968",
journal = "Lecture Notes in Computer Science",
issn = "0302-9743",
publisher = "Springer Verlag",

}

A fuzzy clustering algorithm for analysis of gene expression profiles. / Park, Han Saem; Yoo, Si Ho; Cho, Sung Bae.

In: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), Vol. 3157, 01.12.2004, p. 967-968.

Research output: Contribution to journalConference article

TY - JOUR

T1 - A fuzzy clustering algorithm for analysis of gene expression profiles

AU - Park, Han Saem

AU - Yoo, Si Ho

AU - Cho, Sung Bae

PY - 2004/12/1

Y1 - 2004/12/1

N2 - Advancement of DNA microarray technology has made it possible to get a great deal of biological information by a single experiment. Clustering algorithm is to group genes and reveal their functions or analyze unknown genes, which is categorized into hard and fuzzy clustering. For analyzing DNA microarray, fuzzy clustering can be better since genes can have several genetic information. In this paper, we present the GG (Gath-Geva) algorithm, which is one fuzzy clustering method, for clustering gene expression data. The GG algorithm is an improved version of the fuzzy c-means and GK (Gustafson-Kessel) algorithms and is appropriate for clustering gene expression data that have high dimension and ambiguous distribution. We have clustered serum and yeast data by the GG algorithm and compared it with the fuzzy c-means and GK algorithms. Through these experiments, we confirm that the GG algorithm is better for clustering gene expression data than other two algorithms.

AB - Advancement of DNA microarray technology has made it possible to get a great deal of biological information by a single experiment. Clustering algorithm is to group genes and reveal their functions or analyze unknown genes, which is categorized into hard and fuzzy clustering. For analyzing DNA microarray, fuzzy clustering can be better since genes can have several genetic information. In this paper, we present the GG (Gath-Geva) algorithm, which is one fuzzy clustering method, for clustering gene expression data. The GG algorithm is an improved version of the fuzzy c-means and GK (Gustafson-Kessel) algorithms and is appropriate for clustering gene expression data that have high dimension and ambiguous distribution. We have clustered serum and yeast data by the GG algorithm and compared it with the fuzzy c-means and GK algorithms. Through these experiments, we confirm that the GG algorithm is better for clustering gene expression data than other two algorithms.

UR - http://www.scopus.com/inward/record.url?scp=22944446852&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=22944446852&partnerID=8YFLogxK

M3 - Conference article

VL - 3157

SP - 967

EP - 968

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -