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
  • [31] Integration of gene expression data into genome-scale metabolic models
    Åkesson, M
    Förster, J
    Nielsen, J
    METABOLIC ENGINEERING, 2004, 6 (04) : 285 - 293
  • [32] Cancer Classification of Gene Expression Data using Machine Learning Models
    De Guia, Joseph M.
    Devaraj, Madhavi
    Vea, Larry A.
    2018 IEEE 10TH INTERNATIONAL CONFERENCE ON HUMANOID, NANOTECHNOLOGY, INFORMATION TECHNOLOGY, COMMUNICATION AND CONTROL, ENVIRONMENT AND MANAGEMENT (HNICEM), 2018,
  • [33] Canalizing Zhegalkin polynomials as models for gene expression time series data
    Faisal, Saadia
    Lichtenberg, Gerwald
    Werrier, Herbert
    2006 IEEE INTERNATIONAL CONFERENCE ON ENGINEERING OF INTELLIGENT SYSTEMS, 2006, : 325 - +
  • [34] Gene expression data classification using topology and machine learning models
    Dey, Tamal K.
    Mandal, Sayan
    Mukherjee, Soham
    BMC BIOINFORMATICS, 2022, 22 (SUPPL 10)
  • [35] IntLIM: integration using linear models of metabolomics and gene expression data
    Jalal K. Siddiqui
    Elizabeth Baskin
    Mingrui Liu
    Carmen Z. Cantemir-Stone
    Bofei Zhang
    Russell Bonneville
    Joseph P. McElroy
    Kevin R. Coombes
    Ewy A. Mathé
    BMC Bioinformatics, 19
  • [36] Infinite Plaid Models for Infinite Bi-Clustering
    Ishiguro, Katsuhiko
    Sato, Issei
    Nakano, Masahiro
    Kimura, Akisato
    Ueda, Naonori
    THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2016, : 1701 - 1708
  • [37] A data structure and function classification based method to evaluate clustering models for gene expression data
    易东
    杨梦苏
    黄明辉
    李辉智
    王文昌
    Military Medical Research, 2002, (04) : 312 - 317
  • [38] Using hidden Markov models to analyze gene expression time course data
    Schliep, Alexander
    Schoenhuth, Alexander
    Steinhoff, Christine
    BIOINFORMATICS, 2003, 19 : i255 - i263
  • [39] Two-Exponential Models of Gene Expression Patterns for Noisy Experimental Data
    Alexandrov, Theodore
    Golyandina, Nina
    Holloway, David
    Shlemov, Alex
    Spirov, Alexander
    JOURNAL OF COMPUTATIONAL BIOLOGY, 2018, 25 (11) : 1220 - 1230
  • [40] Evaluating thermodynamic models of enhancer activity on cellular resolution gene expression data
    Samee, Md. Abul Hassan
    Sinha, Saurabh
    METHODS, 2013, 62 (01) : 79 - 90
12345