scholarly journals Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Mingzhou (Joe) Song ◽  
Chris K. Lewis ◽  
Eric R. Lance ◽  
Elissa J. Chesler ◽  
Roumyana Kirova Yordanova ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcriptional Regulation ◽  
Mouse Brain ◽  
Gene Expression Data ◽  
Expression Data ◽  
Temporal Gene Expression

scholarly journals TriGen: A genetic algorithm to mine triclusters in temporal gene expression data

2014 ◽  
Vol 132 ◽  
pp. 42-53 ◽  
Author(s):  
D. Gutiérrez-Avilés ◽  
C. Rubio-Escudero ◽  
F. Martínez-Álvarez ◽  
J.C. Riquelme
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Gene Expression Data ◽  
Expression Data ◽  
Temporal Gene Expression

CLASSIFYING TEMPORAL MICROARRAY DATA BY SELECTING INFORMATIVE GENES

2013 ◽  
Vol 11 (03) ◽  
pp. 1341006
Author(s):  
QIANG LOU ◽  
ZORAN OBRADOVIC
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Microarray Data ◽  
Data Sets ◽  
Temporal Data ◽  
Expression Data ◽  
Selection Methods ◽  
Temporal Gene Expression ◽  
Single Matrix

In order to more accurately predict an individual's health status, in clinical applications it is often important to perform analysis of high-dimensional gene expression data that varies with time. A major challenge in predicting from such temporal microarray data is that the number of biomarkers used as features is typically much larger than the number of labeled subjects. One way to address this challenge is to perform feature selection as a preprocessing step and then apply a classification method on selected features. However, traditional feature selection methods cannot handle multivariate temporal data without applying techniques that flatten temporal data into a single matrix in advance. In this study, a feature selection filter that can directly select informative features from temporal gene expression data is proposed. In our approach, we measure the distance between multivariate temporal data from two subjects. Based on this distance, we define the objective function of temporal margin based feature selection to maximize each subject's temporal margin in its own relevant subspace. The experimental results on synthetic and two real flu data sets provide evidence that our method outperforms the alternatives, which flatten the temporal data in advance.

scholarly journals Principal component tests: applied to temporal gene expression data

2009 ◽  
Vol 10 (Suppl 1) ◽  
pp. S26
Author(s):  
Wensheng Zhang ◽  
Hong-Bin Fang ◽  
Jiuzhou Song
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Principal Component ◽  
Expression Data ◽  
Temporal Gene Expression
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