Boosting signal-to-noise in complex biology: prior knowledge is power.

Boosting signal-to-noise in complex biology: prior knowledge is power.
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DOI:
10.1016/j.cell.2011.03.007
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发表时间:
2011-03-18
期刊:
影响因子:
64.5
通讯作者:
Hood L
Hood L
中科院分区:
生物学1区
文献类型:
--
作者:
Ideker T;Dutkowski J;Hood L

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复杂性是21世纪科学和工程面临的巨大挑战。我们认为,生物学是一门独特的学科,可以通过多种技术来表征分子结构、相互作用和功能,从而解决复杂性问题。分析复杂生物系统的一个主要困难是处理几乎所有大规模生物数据集固有的低信噪比。我们讨论强大的生物信息学概念,通过纳入我们称为过滤器和积分器的处理单元中的外部知识来提高信噪比。这些概念在四项具有里程碑意义的研究中得到了阐述,这些研究提供了滤波器、积分器或两者的模型实现。
Complexity is the grand challenge for science and engineering in the 21st century. We suggest that biology is a discipline that is uniquely situated to tackle complexity, through a diverse array of technologies for characterizing molecular structure, interactions and function. A major difficulty in the analysis of complex biological systems is dealing with the low signal-to-noise inherent to nearly all large-scale biological data sets. We discuss powerful bioinformatic concepts for boosting signal-to-noise through external knowledge incorporated in processing units we call Filters and Integrators. These concepts are illustrated in four landmark studies that have provided model implementations of Filters, Integrators, or both.
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