Plaid models for gene expression data

被引：4
|
作者：
Lazzeroni, L
Owen, A
机构：
[1] Stanford Univ, Sch Med, Dept Hlth Res & Policy, Div Biostat, Stanford, CA 94305 USA
[2] Stanford Univ, Dept Stat, Stanford, CA 94305 USA
来源：
STATISTICA SINICA | 2002年 / 12卷 / 01期
关键词：
microarrays; SVD; transposable data; unsupervised learning;
D O I：
暂无
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Motivated by genetic expression data, we introduce plaid models. These axe a form of two-sided cluster analysis that allows clusters to overlap. Plaid models also incorporate additive two way ANOVA models within the two-sided clusters. Using these models we find interpretable structure in some yeast expression data, as well as in some nutrition data and some foreign exchange data.
引用
下载
收藏
页码：61 / 86
页数：26
相关论文
共 50 条
  • [41] COMBINING GENERALIZED NMF AND DISCRIMINATIVE MIXTURE MODELS FOR CLASSIFICATION OF GENE EXPRESSION DATA
    Liu, Weixiang
    Yuan, Kehong
    Wu, Jian
    Ye, Datian
    Ji, Zhen
    Chen, Siping
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2008, 22 (08) : 1587 - 1598
  • [42] Hierarchical inverse Gaussian models and multiple testing: Application to gene expression data
    Labbe, A
    Thompson, M
    STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, 2005, 4
  • [43] PCA disjoint models for multiclass cancer analysis using gene expression data
    Bicciato, S
    Luchini, A
    Di Bello, C
    BIOINFORMATICS, 2003, 19 (05) : 571 - 578
  • [44] Designing and Evaluating Deep Learning Models for Cancer Detection on Gene Expression Data
    Canakoglu, Arif
    Nanni, Luca
    Sokolovsky, Artur
    Ceri, Stefano
    COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS AND BIOSTATISTICS, CIBB 2018, 2020, 11925 : 249 - 261
  • [45] A new class of mixture models for differential gene expression in DNA microarray data
    Chen, Ming-Hui
    Ibrahim, Joseph G.
    Chi, Yueh-Yun
    JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2008, 138 (02) : 387 - 404
  • [46] Evolving very-compact fuzzy models for gene expression data analysis
    Barreto-Sanz, Miguel Arturo
    Bujard, Alexandre
    Pena-Reyes, Carlos Andres
    IEEE 12TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS & BIOENGINEERING, 2012, : 356 - 361
  • [47] Stochastic models for inferring genetic regulation from microarray gene expression data
    Tian, Tianhai
    BIOSYSTEMS, 2010, 99 (03) : 192 - 200
  • [48] Functional Dissection of Regulatory Models Using Gene Expression Data of Deletion Mutants
    Li, Jin'e
    Liu, Yi
    Liu, Min
    Han, Jing-Dong J.
    PLOS GENETICS, 2013, 9 (09):
  • [49] Mixture models with multiple levels, with application to the analysis of multifactor gene expression data
    Joernsten, Rebecka
    Keles, Suenduez
    BIOSTATISTICS, 2008, 9 (03) : 540 - 554
  • [50] Models of stochastic gene expression
    Paulsson, Johan
    PHYSICS OF LIFE REVIEWS, 2005, 2 (02) : 157 - 175
12345