The complex phenotypes observed during the development of a disease are rarely due to single proteins. Hence, recently, it has been shown that protein networks are a source for identifying powerful biomarkers. These biomarker networks in many cases are more useful in predictions rather than the any individual gene.
Transcriptional modules rich in biomarkers can be generated by measuring coordinately expressed gene expression profiles in biofluids. These “biomarker modules” can further be used to predict new biomarker networks in an iterative manner. Such biomarkers based on networks could also be abstracted from the integrative network models of the cellular networks, which are constructed from high throughput proteomics and genomics datasets.
Stratification of disease progression from one stage to another: For example, the protein networks obtained from expression profile and/or interaction data from a...
- Azuaje F (2010) Disease biomarkers and biological interaction networks. In: Bioinformatics and biomarker discovery: “Omic” data analysis for personalized medicine. Wiley-Blackwell, HobokenGoogle Scholar