Role of clinical bioinformatics in the development of network-based Biomarkers

Disease is a disordered or incorrectly functioning cells, tissue, organ, or system of the body, involved in multiple proteins, cells, organs and systems with the complexity. There still remains the poor understanding of molecular mechanisms by which diseases occur, even though biotechnologies and knowledge on diseases have been improved tremendously. Variations of protein-based biomarkers appear on basis of applications, e.g. functional neuro-imaging biomarkers can play in detecting, diagnosing, assessing treatment response and investigating neurodegenerative disorders [1], which may why the emphasis of much recent work has shifted to network-based biomarkers. The most of preclinical and clinical studies measure systemic levels of one or a few inflammatory proteins as an indicator of pathological alterations or disease severity, while molecular network-based approaches can describe associations between network properties, disease biology and capacity to distinguish between prognostic categories. It was suggested that information encoded in a network of inflammation proteins could predict clinical outcome after myocardial infarction [2].Biomarkers can be gene-, protein-, peptide-, chemical- or physic-based variables. Of those biomarkers, gene- and protein-based ones have been focused and explored mostly from a single gene or protein to multiple genes or proteins, from the expression to functional indication, and from the network to dynamic network, in order to understand a multi-factorial basis responsible for the pathogenesis of diseases. Protein-protein interactions play a central and critical role in many biological functions, mediating the signaling pathways. Network biomarker as a new type of biomarkers with protein-protein interactions was initiated and investigated with the integration of knowledge on protein annotations, interaction, and signaling pathway. It was found that network biomarkers discovered on basis of protein knowledge on the SELDI-TOF-MS data w