Abstract
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.
Keywords: Disease biomarkers, high-throughput, integrative system biology, diagnose, drug, post-genomics, mRNA, cDNA, SAGE, mass spectrometry, nuclear magnetic resonance, bioinformatics, pathologic cells, biochemical pathways, metabolic analysis, genes, probes
Combinatorial Chemistry & High Throughput Screening
Title: Integrative System Biology Strategies for Disease Biomarker Discovery
Volume: 15 Issue: 4
Author(s): Haiyuan Zhang, Hao Hu, Cao Deng, Yeona Chun, Shengtao Zhou, Fuqiang Huang and Qin Zhou
Affiliation:
Keywords: Disease biomarkers, high-throughput, integrative system biology, diagnose, drug, post-genomics, mRNA, cDNA, SAGE, mass spectrometry, nuclear magnetic resonance, bioinformatics, pathologic cells, biochemical pathways, metabolic analysis, genes, probes
Abstract: 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.
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Cite this article as:
Zhang Haiyuan, Hu Hao, Deng Cao, Chun Yeona, Zhou Shengtao, Huang Fuqiang and Zhou Qin, Integrative System Biology Strategies for Disease Biomarker Discovery, Combinatorial Chemistry & High Throughput Screening 2012; 15 (4) . https://dx.doi.org/10.2174/138620712799361852
DOI https://dx.doi.org/10.2174/138620712799361852 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
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