From microarrays to networks: mining expression time series.
From microarrays to networks: mining expression time series.
复制标题
从微阵列到网络：挖掘表达时间序列。
DOI：
10.1016/s1359-6446(02)02440-6
复制
发表时间：
2002
影响因子：
7.4
通讯作者：
Dewey,TGregory
中科院分区：
文献类型：
--
作者：
Dewey,TGregory
Over the past few years, powerful new methods have been devised that enable researchers to study the expression dynamics of many genes simultaneously (e.g. gene expression profiles using cDNA microarrays). In principle, this potentially vast quantity of data enables the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. Finding the patterns in those data represents the next major phase in our understanding of the programming and functioning of the living cell. Simple dynamic models can be used to generate gene expression networks. These networks reveal the phenomenological link between the expression of different genes. This review discuss how these networks are generated and outlines several data-mining techniques for extracting relationships and hypotheses in gene expression. These emerging methods can be applied to a range of biological problems.
