One of the driving force that pushes a normal cell towards a cancerous state is led by unregulated control of its cellular division through the suppression of tumour suppressor genes and over expression of oncogenes. These cancer-driver genes have been associated in the advancement of different cancer types, though how they are represented and with which genes they are associated within the cancer’s gene expression profiles is a boon towards understanding of said cancer’s development. An novel way of modelling these gene expression profiles is to use graphlet-based network analysis, a data mining technique that allows for identification, understanding, and prediction of their functionality, emergent properties, and potential controllability. The thesis aims to identify patterns in gene co-expression networks of shared cancer census genes found in breast cancer and lung cancer using cancer gene census data provided by COSMIC, the Catalogue of Somatic Mutations in Cancer.
