With the recent progress in nanofabrication technologies, plasmonic nanostructures offer a highly promising pathway to discovering new phenomena and developing novel optical devices.
Such data are currently being used to identify cancer-driver genes and pathways, to discover molecular targets for new therapies, and to define molecular profiles that characterize clinically meaningful patient categories.
We developed an algorithm called NetPathID (NETwork based method for PATHway IDentification) to discover pathways disrupted by copy number alterations across cancers.
LinkinPath extracts the frequently occurred subgraphs /subnetworks from the pathway networks to discover the commonalities across the datasets (Supplementary Fig. S9).
The use of systems biology in correlation or co-expression-based approaches, have been utilized successfully by the metabolic engineering field to fill gaps in biosynthetic pathways, or to discover novel regulators of biosynthetic pathways.
Consequently, microarray technology is employed to identify up or down regulated genes that play a role on the specific cancers, activation of oncogenic pathways, and to discover novel biomarkers for the clinical diagnosis [2].
Gene set analysis utilizes known knowledge of gene sets, such as gene pathways [4], to discover gene sets the expressions of which are associated with a phenotype of interest.
Molecular homogeneity may be essential in order to identify specific biological pathways affected, to discover precise drug targets in each subgroup or to obtain individual survival classifiers.
These cells serve as discovery and screening platforms to identify common signalling pathways and to discover small molecule drugs and molecular therapies that potentially reverse the disease phenotype.
