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 method is shown to be able to correlate sequences with function in the human proteome database,  and might find other useful applications for structure discovery also in computational linguistics, in the analysis of time series, and in the theory of dynamical systems .
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 might find other useful applications to process large amount of data in various fields .
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method
analysis of communities of networks of motifs
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function
functions
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to detect hot topics from online social dialogs, to characterize trajectories of dynamical systems,
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for structure discovery also in computational linguistics, in the analysis of time series, and in the theory of dynamical systems
to process large amount of data in various fields