ArticleMathematical biosciences2008
WSE, a new sequence distance measure based on word frequencies.
Article in Mathematical biosciences, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- K2 and K2*: efficient alignment-free sequence similarity measurement based on Kendall statistics.Bioinformatics (Oxford, England) · 2018Article
- An improved alignment-free model for DNA sequence similarity metric.BMC bioinformatics · 2014Article
- A novel hierarchical clustering algorithm for gene sequences.BMC bioinformatics · 2012Article
- Phylogenetic analysis of protein sequences based on distribution of length about common sub-string.The protein journal · 2011Article
- Using Gaussian model to improve biological sequence comparison.Journal of computational chemistry · 2010Article
- A Poisson model of sequence comparison and its application to coronavirus phylogeny.Mathematical biosciences · 2009Article
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Authors and funding
2 authors.
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Abstract
In this article, we present a new distance metric, the Weighted Sequence Entropy (WSE), based on the short word composition of biological sequences. As a revision of the classical relative entropy (RE), our metric (1) works equivalently with RE in the case of small k, (2) avoids the degeneracy when some word types are absent in one sequence but not in the other. Experiments on 25 viruses including SARS-CoVs show that our method and RE give exactly the same phylogenetic tree when word length k <or= 3. When k>3, our method still works and gets convergent phylogenetic topology but the RE gives degenerate results.
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