Context-specific gene regulations in cancer gene expression data.
Learning or inferring networks of genomic regulation specific to a cellular state, such as a subtype of tumor, can yield insight above and beyond that resulting from network-learning techniques which do not acknowledge the adaptive nature of the cellular system. In this study we show that Cellular Context Mining, which is based on a mathematical model of contextual genomic regulation, produces gene regulatory networks (GRNs) from steady-state expression microarray data which are specific to the varying cellular contexts hidden in the data; we show that these GRNs not only model gene interactions, but that they are also readily annotated with context-specific genomic information. We propose that these context-specific GRNs provide advantages over other techniques, such as clustering and Bayesian networks, when applied to gene expression data of cancer patients.
Sen
Ina
I
School of Computing and Informatics, Arizona State University, 699 South Mill Avenue, Suite 553, Tempe, AZ 85281, USA.
Verdicchio
Michael P
MP
Jung
Sungwon
S
Trevino
Robert
R
Bittner
Michael
M
Kim
Seungchan
S
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001699:Biometry; D016000:Cluster Analysis; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D008957:Models, Genetic; D005810:Multigene Family; D009369:Neoplasms / Q000145:classification / Q000235:genetics*