Zhang Haiyuan, Hu Hao, Deng Cao, Chun Yeona, Zhou Shengtao, Huang Fuqiang, Zhou Qin
Medical School, Yangtze University, Jingzhou Hubei 434000, People's Republic of China.
Comb Chem High Throughput Screen. 2012 May 1;15(4):286-98. doi: 10.2174/138620712799361852.
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.
生物标志物目前被广泛用于疾病诊断、治疗监测和评估潜在的候选药物。差异组学研究加速了生物标志物的发现进程。通过整合从 “组学” 中提取生物学知识,整合系统生物学不仅可以识别人类疾病生物标志物,还能创建细胞、器官、生化过程和完整生物体的预测模型。高通量方法的最新发展使得能够以前所未有的规模分析基因组、转录组、蛋白质组和代谢组,从而导致众多公共数据库中的实验数据大量涌现。已经开发了几种整合系统生物学方法并将其应用于从数据库中发现疾病生物标志物。在本综述中,我们重点介绍其中的几种方法,并确定整合系统生物学领域背景下的未来发展方向。