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.

Mining molecular contexts of cancer via in-silico conditioning.

Cell maintains its specific status by tightly regulating a set of genes through various regulatory mechanisms. If there are aberrations that force cell to adjust its regulatory machinery away from the normal state to reliably provide proliferative signals and abrogate normal safeguards, it must achieve a new regulatory state different from the normal. Due to this tightly coordinated regulation, the expression of genes should show consistent patterns within a cellular context, for example, a subtype of tumor, but the behaviour of those genes outside the context would rather become less consistent.