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N-grams are also regularly used in bioinformatics. For example you can split a genome sequence into words of length k, where we use the term 'k-mers'. You can u

N-grams are also regularly used in bioinformatics. For example you can split a genome sequence into words of length k, where we use the term 'k-mers'. You can use a k-mer frequency table as a heuristic where ever performing a full sequence analysis is too computationally expensive. For example if two genomes A <-> B have a smaller euclidean distance in their respective kmer tables than A <-> C you might assume that A and B are more closely related in evolutionary distance. This is just one example and kmers are widely used through out the bioinformatics field. For instance, the wikipedia entry for velvet has a description on how they can be used for genome assembly: http://en.wikipedia.org/wiki/Velvet_assembler http://en.wikipedia.org/wiki/Velvet_assembler

dave_sullivan 12y ago
Have you by any chance taken a look at applying NLP techniques like generating sentence vectors with recurrent neural networks to this type of data? 2+ pair mappings (ngrams) have been in use successfully for a while in NLP, but they don't know about any context outside the current ngram. Recent work has involved creating word and sentence vectors that capture much richer dynamics and this is where a lot of the excitement is coming from in the deep learning world wrt text (of any language, maybe even computer code, as it turns out) I've been wanting to try applying these machine learning techniques to this type of problem (could be for clustering--which was your example--classification, regression, or sample generation--of eg novel and probable sequences?) If you or anyone here as an interest in this sort of thing on the bio side, please contact me (email in profile)