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
  • [41] Classification of Leukemia Gene Expression Data Using Particle Swarm Optimization
    Liu, Yajie
    Shi, Xinling
    An, Zhenzhou
    2012 SIXTH INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING (ICGEC), 2012, : 241 - 244
  • [42] Gene selection for tumor classification using microarray gone expression data
    Yendrapalli, K.
    Basnet, R.
    Mukkamala, S.
    Sung, A. H.
    WORLD CONGRESS ON ENGINEERING 2007, VOLS 1 AND 2, 2007, : 290 - +
  • [43] A fuzzy intelligent approach to the classification problem in gene expression data analysis
    Khashei, Mehdi
    Hamadani, Ali Zeinal
    Bijari, Mehdi
    KNOWLEDGE-BASED SYSTEMS, 2012, 27 : 465 - 474
  • [44] Disjoint PCA models for marker identification and classification of cancer types using gene expression data
    Bicciato, S
    Luchini, A
    Di Bello, C
    MINERVA BIOTECNOLOGICA, 2002, 14 (3-4) : 281 - 290
  • [45] An improved FMM neural network for classification of gene expression data
    Juan, Liu
    Fei, Luo
    Yongqiong, Zhu
    FUZZY INFORMATION AND ENGINEERING, PROCEEDINGS, 2007, 40 : 65 - +
  • [46] A Hybrid Data Gravitation based Classification Algorithm Applied to Gene Expression Data
    Yeh, Wei-Chang
    Lee, Yen-Chin
    Lai, Chyh-Ming
    Shih, Yan-Chih
    Huang, Hsin-Ping
    Jiang, Yunzhi
    2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD), 2017,
  • [47] Gene Expression Data Classification Using Laplacian Eigenmap Based on Improved Maximum Margin Criterion
    Wang Nian
    Wang Junsheng
    Ge Fang
    CHINESE JOURNAL OF ELECTRONICS, 2013, 22 (03): : 521 - 524
  • [48] RPCA-Based Tumor Classification Using Gene Expression Data
    Liu, Jin-Xing
    Xu, Yong
    Zheng, Chun-Hou
    Kong, Heng
    Lai, Zhi-Hui
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2015, 12 (04) : 964 - 970
  • [49] Comparative Analysis of Discretization Methods for Gene Selection of Breast Cancer Gene Expression Data
    Sathishkumar, E. N.
    Thangavel, K.
    Nishama, A.
    COMPUTATIONAL INTELLIGENCE, CYBER SECURITY AND COMPUTATIONAL MODELS, 2014, 246 : 373 - 378
  • [50] Spatial clustering based gene selection for gene expression analysis in microarray data classification
    Dhas, P. Edwin
    Lalitha, S.
    Govindaraj, Annalakshmi
    Jyoshna, B.
    AUTOMATIKA, 2024, 65 (01) : 152 - 158
12345