A novel non-overlapping bi-clustering algorithm for network generation using living cell array data

Motivation: The living cell array quantifies the contribution of activated transcription factors upon the expression levels of their target genes. The direct manipulation of the regulatory mechanisms offers enormous possibilities for deciphering the machinery that activates and controls gene express...

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Bibliographic Details
Published in:Bioinformatics 2007-09, Vol.23 (17), p.2306-2313
Main Authors: Yang, E., Foteinou, P.T., King, K.R., Yarmush, M.L., Androulakis, I.P.
Format: Article
Language:English
Subjects:
Biological and medical sciences
Biological Assay - methods
Cell Physiological Phenomena
Cluster Analysis
Fundamental and applied biological sciences. Psychology
Gene Expression Profiling - methods
General aspects
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Microfluidic Analytical Techniques - methods
Signal Transduction - physiology
Transcription Factors - metabolism
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Summary:Motivation: The living cell array quantifies the contribution of activated transcription factors upon the expression levels of their target genes. The direct manipulation of the regulatory mechanisms offers enormous possibilities for deciphering the machinery that activates and controls gene expression. We propose a novel bi-clustering algorithm for generating non-overlapping clusters of reporter genes and conditions and demonstrate how this information can be interpreted in order to assist in the construction of transcription factor interaction networks. Contact: Yannis@rci.rutgers.edu
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btm335