A game theoretical approach to the classification problem in gene expression data analysis

被引:27
|
作者
Fragnelli, Vito [1 ]
Moretti, Stefano [1 ]
机构
[1] Natl Inst Canc Res, Unit Mol Epidemiol, I-16132 Genoa, Italy
关键词
gene expression analysis; cooperative game; classification problem; shapley value; interaction index;
D O I
10.1016/j.camwa.2006.12.088
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Microarray technology allows for the evaluation of the level of expression of thousands of genes in a sample of cells under a given condition. In this paper, we introduce a methodology based on cooperative Game Theory for the selection of groups of genes with high power in classifying samples, according to gene expression patterns. The connection between microarray games and classification games is discussed and the use of the Shapley value to measure the power of genes for classification is motivated on particular instances and compared to the interaction index. (c) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码:950 / 959
页数:10
相关论文
共 50 条
  • [21] Analysis of gene expression data by the logic minimization approach
    Gamberger, D
    Lavrac, N
    ARTIFICIAL INTELLIGENCE IN MEDICINE, PROCEEDINGS, 2003, 2780 : 244 - 248
  • [22] A Computational Approach to Gene Expression Data Extraction and Analysis
    Alan Wee-Chung Liew
    Lap Keung Szeto
    Sy-Sen Tang
    Hong Yan
    Mengsu Yang
    Journal of VLSI signal processing systems for signal, image and video technology, 2004, 38 : 237 - 258
  • [23] Gene Expression Data Classification by VVRKFA
    Ghorai, Santanu
    Mukherjee, Anirban
    Dutta, Pranab K.
    2ND INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION, CONTROL AND INFORMATION TECHNOLOGY (C3IT-2012), 2012, 4 : 330 - 335
  • [24] Fuzzy classification of gene expression data
    Schaefer, Gerald
    Nakashima, Tomoharu
    Yokota, Yasuyuki
    Ishibuchi, Hisao
    2007 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-4, 2007, : 1095 - +
  • [25] An ensemble approach for phenotype classification based on fuzzy partitioning of gene expression data
    Dragomir, A.
    Maraziotis, I.
    Bezerianos, A.
    2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15, 2006, : 1930 - +
  • [26] A New Filter Approach Based on Effective Ranges for Classification of Gene Expression Data
    Turfan, Derya
    Altunkaynak, Bulent
    Yeniay, Ozgur
    BIG DATA, 2024, 12 (04) : 312 - 330
  • [27] Optimal approach for classification of acute leukemia subtypes based on gene expression data
    Cho, JH
    Lee, D
    Park, JH
    Kim, K
    Lee, IB
    BIOTECHNOLOGY PROGRESS, 2002, 18 (04) : 847 - 854
  • [28] A Discriminative Feature Extraction Approach for Tumor Classification Using Gene Expression Data
    Mei, Qinglin
    Zhang, Huaxiang
    Liang, Cheng
    CURRENT BIOINFORMATICS, 2016, 11 (05) : 561 - 570
  • [29] 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
  • [30] Gene Expression Data Classification Using Consensus Independent Component Analysis
    Chun-Hou Zheng1
    2 Intelligent Computing Lab
    Genomics Proteomics & Bioinformatics, 2008, (02) : 74 - 82