Dipartimento di Fisica ed Astronomia, Università di Catania, INFN, Italy.
Phys Rev Lett. 2010 Oct 22;105(17):178702. doi: 10.1103/PhysRevLett.105.178702. Epub 2010 Oct 19.
We introduce a method to convert an ensemble of sequences of symbols into a weighted directed network whose nodes are motifs, while the directed links and their weights are defined from statistically significant co-occurences of two motifs in the same sequence. The analysis of communities of networks of motifs is shown to be able to correlate sequences with functions in the human proteome database, to detect hot topics from online social dialogs, to characterize trajectories of dynamical systems, and it might find other useful applications to process large amounts of data in various fields.
我们介绍了一种方法，可将符号序列的集合转换为加权有向网络，其中节点是基序，而有向链接及其权重则是根据同一序列中两个基序的统计显著共现来定义的。对基序网络的社区进行分析表明，它能够将序列与人类蛋白质组数据库中的功能相关联，从在线社交对话中检测热门话题，刻画动态系统的轨迹，并且可能在各个领域处理大量数据时找到其他有用的应用。
