Bootstrapping Time-Course Gene Expression Data for Gene Networks: Application to Gene Relevance Networks

2018 ◽  
Vol 25 (12) ◽  
pp. 1374-1384
Author(s):  
Jeonifer M. Garren ◽  
Jaejik Kim
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Time Course ◽  
Expression Data

scholarly journals Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data

2007 ◽  
Vol 8 (1) ◽  
Author(s):  
Miika Ahdesmäki ◽  
Harri Lähdesmäki ◽  
Andrew Gracey ◽  
llya Shmulevich ◽  
Olli Yli-Harja
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Time Course ◽  
Robust Regression ◽  
Expression Data ◽  
Periodicity Detection

Building Gene Networks by Analyzing Gene Expression Profiles

2019 ◽  
pp. 27-44
Author(s):  
Crescenzio Gallo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Gene Expressions ◽  
Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

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