Integrative Systems Biology for Data-Driven Knowledge Discovery

被引:11
|
作者
Greene, Casey S.
Troyanskaya, Olga G. [1 ,2 ]
机构
[1] Princeton Univ, Dept Comp Sci, Princeton, NJ 08544 USA
[2] Princeton Univ, Lewis Sigler Inst Integrat Genom, Carl Icahn Lab 228, Princeton, NJ 08544 USA
基金
美国国家卫生研究院;
关键词
Functional genomics; bioinformatics; Bayesian integration; HIGH-THROUGHPUT; GENE-EXPRESSION; RNA-SEQ; GENOME; NETWORK; SCALE; TOOL; IDENTIFICATION; PREDICTION; SCLEROSIS;
D O I
10.1016/j.semnephrol.2010.07.002
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
Integrative systems biology is an approach that brings together diverse high-throughput experiments and databases to gain new insights into biological processes or systems at molecular through physiological levels. These approaches rely on diverse high-throughput experimental techniques that generate heterogeneous data by assaying varying aspects of complex biological processes. Computational approaches are necessary to provide an integrative view of these experimental results and enable data-driven knowledge discovery. Hypotheses generated from these approaches can direct definitive molecular experiments in a cost-effective manner. By using integrative systems biology approaches, we can leverage existing biological knowledge and large-scale data to improve our understanding of as yet unknown components of a system of interest and how its malfunction leads to disease. © 2010.
引用
收藏
页码:443 / 454
页数:12
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