Network biology methods integrating biological data for translational science

被引:41
作者
Bebek, Gurkan [1 ]
Koyutuerk, Mehmet
Price, Nathan D. [2 ,3 ,4 ]
Chance, Mark R.
机构
[1] Case Western Reserve Univ, Ctr Prote & Bioinformat, Cleveland, OH 44106 USA
[2] Inst Syst Biol, Seattle, WA USA
[3] Univ Washington, Dept Bioengn, Seattle, WA 98195 USA
[4] Univ Washington, Dept Comp Sci & Engn, Seattle, WA 98195 USA
基金
美国国家科学基金会;
关键词
network biology; bioinformatics; GENOME-WIDE ASSOCIATION; GENE-EXPRESSION; DYSREGULATED SUBNETWORKS; ENRICHMENT ANALYSIS; PROTEIN COMPLEXES; RECONSTRUCTION; DISEASE; IDENTIFICATION; MODELS; PRIORITIZATION;
D O I
10.1093/bib/bbr075
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.
引用
收藏
页码:446 / 459
页数:14
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