Biomarkers are currently widely used to diagnose diseases, monitor treatments, 
and evaluate potential drug candidates. Research of differential Omics 
accelerate the advancements of biomarkers' discovery. By extracting biological 
knowledge from the 'omics' through integration, integrative system biology 
creates predictive models of cells, organs, biochemical processes and complete 
organisms, in addition to identifying human disease biomarkers. Recent 
development in high-throughput methods enables analysis of genome, 
transcriptome, proteome, and metabolome at an unprecedented scale, thus 
contributing to the deluge of experimental data in numerous public databases. 
Several integrative system biology approaches have been developed and applied to 
the discovery of disease biomarkers from databases. In this review, we highlight 
several of these approaches and identify future steps in the context of the 
field of integrative system biology.