Exploiting big biology: integrating large-scale biological data for function inference.
Exploiting big biology: integrating large-scale biological data for function inference.
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DOI：
10.1093/bib/2.4.363
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发表时间：
2001-12-01
影响因子：
9.5
通讯作者：
Date, S
中科院分区：
文献类型：
--
作者：
Marcotte, E;Date, S
分子生物学家产生的数据量正以指数速度增长。一些增长最快的数据集是基因表达的测量，在数量上只有基因序列和大量的生物学文献可以与之相比。基因表达数据和序列数据都为数千个新发现基因的功能提供了线索，但都没有给出完整的答案。因此，很多努力都集中在整合这些大型数据集，并将它们与所有可用的功能数据结合起来，以推断未表征基因的功能。这篇综述讨论了全基因组功能推断中最相关的功能数据，并描述了几种整合这些不同数据类型的方法。
The amount of data produced by molecular biologists is growing at an exponential rate. Some of the fastest growing sets of data are measurements of gene expression, comparable in quantity only to gene sequences and the vast biological literature. Both gene expression data and sequence data offer hints as to the functions of thousands of newly discovered genes, but neither give complete answers. Therefore, much effort is being focused on integrating these large data sets and combining them with all available functional data to draw inferences about the functions of uncharacterised genes. This review discusses the most pertinent functional data for genome-wide functional inference and describes several methods by which these disparate data types are being integrated.