Gene Expression Data Classification Using Independent Variable Group Analysis

被引：0
|
作者：
Zheng, Chunhou [2 ,3 ]
Zhang, Lei [1 ]
Li, Bo [3 ]
Xu, Min [2 ]
机构：
[1] Hong Kong Polytech Univ, Dept Comp, Biometr Res Ctr, Hong Kong, Hong Kong, Peoples R China
[2] Qufu Normal Univ, Coll Informat & Commun Technol, Shandong Sheng 276826, Peoples R China
[3] Chinese Acad Sci, Inst Machine Intelligence, Intelligent Comp Lab, Hefei 230031, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2008, PT 2, PROCEEDINGS | 2008年 / 5264卷
基金：
美国国家科学基金会; 中国博士后科学基金;
关键词：
Gene expression data; Independent variable group analysis; Gene selection; Classification;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Microarrays are capable of detecting the expression levels of thousands of genes simultaneously. In this paper, a new method for gene selection based on independent variable group analysis is proposed. In this method. we first used t-statistics method to select a part of genes from the original data. Then we selected the key genes from the selected genes by t-statistics for tumor classification using IVGA. Finally, we used SVM to classify tumors based on the key genes selected using IVGA. To validate the efficiency, the proposed method is applied to classify three different DNA microarray data sets. The prediction results show that our method is efficient and feasible.
引用
收藏
页码：243 / +
页数：3
相关论文
共 50 条
  • [1] Gene selection using independent variable group analysis for tumor classification
    Chun-Hou Zheng
    Yan-Wen Chong
    Hong-Qiang Wang
    Neural Computing and Applications, 2011, 20 : 161 - 170
  • [2] Gene selection using independent variable group analysis for tumor classification
    Zheng, Chun-Hou
    Chong, Yan-Wen
    Wang, Hong-Qiang
    NEURAL COMPUTING & APPLICATIONS, 2011, 20 (02) : 161 - 170
  • [3] Gene Expression Data Classification Using Consensus Independent Component Analysis
    Chun-Hou Zheng1
    2 Intelligent Computing Lab
    Genomics Proteomics & Bioinformatics, 2008, (02) : 74 - 82
  • [4] Group K-SVD for the classification of gene expression data
    He, Ping
    Fan, Baichuan
    Xu, Xiaohua
    Ding, Jie
    Liang, Yali
    Lou, Yuan
    Zhang, Zhijun
    Chang, Xincheng
    COMPUTERS & ELECTRICAL ENGINEERING, 2019, 76 (143-153) : 143 - 153
  • [5] GENE EXPRESSION DATA CLASSIFICATION AND PATTERN ANALYSIS USING DATA DRIVEN APPROACH
    Ramisa, Aiman Jabeen
    Hossain, Ananna
    Islam, S. K. Md Injamul
    Swadesh, Ponuel Mollah
    Islam, Md Toushif
    Rahman, Md Anisur
    Parvez, Mohammad Zavid
    PROCEEDINGS OF 2021 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), 2021, : 82 - 90
  • [6] Cancer classification using gene expression data
    Lu, Y
    Han, JW
    INFORMATION SYSTEMS, 2003, 28 (04) : 243 - 268
  • [7] Using uncorrelated discriminant analysis for tissue classification with gene expression data
    Ye, JP
    Li, T
    Xiong, T
    Janardan, R
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2004, 1 (04) : 181 - 190
  • [8] Gene expression data classification using locally linear discriminant embedding
    Li, Bo
    Zheng, Chun-Hou
    Huang, De-Shuang
    Zhang, Lei
    Han, Kyungsook
    COMPUTERS IN BIOLOGY AND MEDICINE, 2010, 40 (10) : 802 - 810
  • [9] A note on classification of gene expression data using support vector machines
    Fujarewicz, K
    Kimmel, M
    Rzeszowska-Wolny, J
    Swierniak, A
    JOURNAL OF BIOLOGICAL SYSTEMS, 2003, 11 (01) : 43 - 56
  • [10] Analysis of complexity indices for classification problems: Cancer gene expression data
    Lorena, Ana C.
    Costa, Ivan G.
    Spolaor, Newton
    de Souto, Marcilio C. P.
    NEUROCOMPUTING, 2012, 75 (01) : 33 - 42
12345