Clustering of gene expression data: Performance and similarity analysis

被引：0
作者：
Yin, Longde [1 ]
Huang, Chun-Hsi [1 ]
机构：
[1] Univ Connecticut, Dept Comp Sci & Engn, Storrs, CT 06269 USA
来源：
FIRST INTERNATIONAL MULTI-SYMPOSIUMS ON COMPUTER AND COMPUTATIONAL SCIENCES (IMSCCS 2006), PROCEEDINGS, VOL 1 | 2006年
关键词：
clustering algorithms; gene expression; microarray; cluster similarity analysis; performance study;
D O I：
10.1109/IMSCCS.2006.43
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Recent advances of the DNA Microarray technology allow monitoring gene expression profiles of thousands of genes simultaneously. However, the analysis and handling of such fast growing data is becoming the major bottleneck in the utilization of the technology. Clustering analysis is one of the most effective methods for analyzing such gene expression data. In this paper we first experimentally study three major clustering algorithms: Hierarchical Clustering, Self-Organizing Map (SOM), and Self Organizing Tree Algorithm (SOTA), using Yeast Saccharomyces cerevisiae gene expression data, and compare their performance. Then, we present a data mining tool, Cluster Diff, which allows the similarity analysis of clusters generated by different algorithms. A case study is conducted based on clusters generated by SOTA and SOM.
引用
收藏
页码：142 / +
页数：3
相关论文
共 50 条
  • [11] On the selection of appropriate distances for gene expression data clustering
    Pablo A Jaskowiak
    Ricardo JGB Campello
    Ivan G Costa
    BMC Bioinformatics, 15
  • [12] A pattern matching approach for clustering gene expression data
    Das, Rosy
    Kalita, Jugal
    Bhattacharyya, Dhruba K.
    INTERNATIONAL JOURNAL OF DATA MINING MODELLING AND MANAGEMENT, 2011, 3 (02) : 130 - 149
  • [13] Techniques for clustering gene expression data
    Kerr, G.
    Ruskin, H. J.
    Crane, M.
    Doolan, P.
    COMPUTERS IN BIOLOGY AND MEDICINE, 2008, 38 (03) : 283 - 293
  • [14] An Incremental Clustering of Gene Expression data
    Das, Rosy
    Bhattacharyya, Dhruba K.
    Kalita, Jugal K.
    2009 WORLD CONGRESS ON NATURE & BIOLOGICALLY INSPIRED COMPUTING (NABIC 2009), 2009, : 741 - +
  • [15] Gene expression data analysis
    Brazma, A
    Vilo, J
    FEBS LETTERS, 2000, 480 (01) : 17 - 24
  • [16] Robust complementary hierarchical clustering for gene expression data analysis by β-divergence
    Badsha, Md. Bahadur
    Mollah, Md. Nurul Hague
    Jahan, Nusrat
    Kurata, Hiroyuki
    JOURNAL OF BIOSCIENCE AND BIOENGINEERING, 2013, 116 (03) : 397 - 407
  • [17] Algorithm for Clustering Analysis of Gene Expression Data using MapReduce Framework
    Priya, P. Packia Amutha
    Lawrance, R.
    2016 INTERNATIONAL CONFERENCE ON COMPUTING TECHNOLOGIES AND INTELLIGENT DATA ENGINEERING (ICCTIDE'16), 2016,
  • [18] Semi-supervised consensus clustering for gene expression data analysis
    Yunli Wang
    Youlian Pan
    BioData Mining, 7
  • [19] A Hierarchical Approach for Clustering and Pattern Matching of Gene Expression Data
    Hoque, Soriful
    Istyaq, Salim
    Riaz, Md Mushir
    2012 SIXTH INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING (ICGEC), 2012, : 413 - 416
  • [20] Evaluation of clustering algorithms for gene expression data using gene ontology annotations
    Ma Ning
    Zhang Zheng-guo
    CHINESE MEDICAL JOURNAL, 2012, 125 (17) : 3048 - 3052
12345