Analyzing array data using supervised methods

被引:45
作者
Ringnér, M
Peterson, C
Khan, J
机构
[1] NHGRI, Canc Genet Branch, NIH, Bethesda, MD 20892 USA
[2] Lund Univ, Dept Theoret Phys, Complex Syst Div, SE-22362 Lund, Sweden
[3] NCI, Ctr Adv Technol, NIH, Gaithersburg, MD 20877 USA
关键词
artificial neural networks; bioinformatics; diagnostic classification; diagnostic prediction; DNA chip; drug targets; genes; machine learning microarray; support vector machines; target identification;
D O I
10.1517/14622416.3.3.403
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Pharmacogenomics is the application of genomic technologies to drug discovery and development, as well as for the elucidation of the mechanisms of drug action on cells and organisms. DNA microarrays measure genome-wide gene expression patterns and are an important tool for pharmacogenomic applications, such as the identification of molecular targets for drugs, toxicological studies and molecular diagnostics. Genome-wide investigations generate vast amounts of data and there is a need for computational methods to manage and analyze this information. Recently, several supervised methods, in which other information is utilized together with gene expression data, have been used to characterize genes and samples. The choice of analysis methods will influence the results and their interpretation, therefore it is important to be familiar with each method, its scope and limitations. Here, methods with special reference to applications for pharmacogenomics are reviewed.
引用
收藏
页码:403 / 415
页数:13
相关论文
共 55 条
  • [1] Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
    Alizadeh, AA
    Eisen, MB
    Davis, RE
    Ma, C
    Lossos, IS
    Rosenwald, A
    Boldrick, JG
    Sabet, H
    Tran, T
    Yu, X
    Powell, JI
    Yang, LM
    Marti, GE
    Moore, T
    Hudson, J
    Lu, LS
    Lewis, DB
    Tibshirani, R
    Sherlock, G
    Chan, WC
    Greiner, TC
    Weisenburger, DD
    Armitage, JO
    Warnke, R
    Levy, R
    Wilson, W
    Grever, MR
    Byrd, JC
    Botstein, D
    Brown, PO
    Staudt, LM
    [J]. NATURE, 2000, 403 (6769) : 503 - 511
  • [2] Allander SV, 2001, CANCER RES, V61, P8624
  • [3] Predictive non-linear modeling of complex data by artificial neural networks
    Almeida, JS
    [J]. CURRENT OPINION IN BIOTECHNOLOGY, 2002, 13 (01) : 72 - 76
  • [4] MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia
    Armstrong, SA
    Staunton, JE
    Silverman, LB
    Pieters, R
    de Boer, ML
    Minden, MD
    Sallan, SE
    Lander, ES
    Golub, TR
    Korsmeyer, SJ
    [J]. NATURE GENETICS, 2002, 30 (01) : 41 - 47
  • [5] Arteaga CL, 2001, J CLIN ONCOL, V19, p32S
  • [6] Tissue classification with gene expression profiles
    Ben-Dor, A
    Bruhn, L
    Friedman, N
    Nachman, I
    Schummer, M
    Yakhini, Z
    [J]. JOURNAL OF COMPUTATIONAL BIOLOGY, 2000, 7 (3-4) : 559 - 583
  • [7] BENDOR A, 2000, AGL200013 AG TECH
  • [8] Bishop C. M., 1995, NEURAL NETWORKS PATT
  • [9] Molecular classification of cutaneous malignant melanoma by gene expression profiling
    Bittner, M
    Meitzer, P
    Chen, Y
    Jiang, Y
    Seftor, E
    Hendrix, M
    Radmacher, M
    Simon, R
    Yakhini, Z
    Ben-Dor, A
    Sampas, N
    Dougherty, E
    Wang, E
    Marincola, F
    Gooden, C
    Lueders, J
    Glatfelter, A
    Pollock, P
    Carpten, J
    Gillanders, E
    Leja, D
    Dietrich, K
    Beaudry, C
    Berens, M
    Alberts, D
    Sondak, V
    Hayward, N
    Trent, J
    [J]. NATURE, 2000, 406 (6795) : 536 - 540
  • [10] Comprehensive analysis of photoreceptor gene expression and the identification of candidate retinal disease genes
    Blackshaw, S
    Fraioli, RE
    Furukawa, T
    Cepko, CL
    [J]. CELL, 2001, 107 (05) : 579 - 589