A novel non-overlapping bi-clustering algorithm for network generation using living cell array data.
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.
Yang
E
E
Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA.
Foteinou
P T
PT
King
K R
KR
Yarmush
M L
ML
Androulakis
I P
IP
D001681:Biological Assay / Q000379:methods*; D002468:Cell Physiological Phenomena; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D046210:Microfluidic Analytical Techniques / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology*; D014157:Transcription Factors / Q000378:metabolism*