Efficient Biclustering Algorithms for Time Series Gene Expression Data Analysis

被引：0
|
作者：
Madeira, Sara C. [1 ]
Oliveira, Arlindo L. [1 ]
机构：
[1] Univ Tecn Lisbon, Inst Super Tecn, Lisbon, Portugal
来源：
DISTRIBUTED COMPUTING, ARTIFICIAL INTELLIGENCE, BIOINFORMATICS, SOFT COMPUTING, AND AMBIENT ASSISTED LIVING, PT II, PROCEEDINGS | 2009年 / 5518卷
关键词：
Biclustering; gene expression time series; temporal expression patterns; anticorrelated time-lagged patterns; regulatory modules;
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：
We present a summary of a PhD thesis proposing efficient biclustering algorithms for time series gene expression data analysis, able to discover important aspects of gene regulation as anticorrelation and time-lagged relationships, and a scoring method based oil statistical significance and similarity measures. The ability of the proposed algorithms to efficiently identify sets of genes with statistically significant and biologically meaningful expression patterns is shown to be instrumental in the discovery of relevant biological phenomena, leading to more convincing evidence of specific transcriptional regulatory mechanisms.
引用
收藏
页码：1013 / 1019
页数：7
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