Please use this identifier to cite or link to this item: https://research.matf.bg.ac.rs/handle/123456789/500
Title: n-gram-based classification and unsupervised hierarchical clustering of genome sequences
Authors: Tomović, Andrija
Janičić, Predrag 
Keselj, Vlado
Affiliations: Informatics and Computer Science 
Keywords: Classification;Genome sequence;Hierarchical clustering;n-Gram
Issue Date: 2006
Journal: Computer methods and programs in biomedicine
Abstract: 
In this paper we address the problem of automated classification of isolates, i.e., the problem of determining the family of genomes to which a given genome belongs. Additionally, we address the problem of automated unsupervised hierarchical clustering of isolates according only to their statistical substring properties. For both of these problems we present novel algorithms based on nucleotide n-grams, with no required preprocessing steps such as sequence alignment. Results obtained experimentally are very positive and suggest that the proposed techniques can be successfully used in a variety of related problems. The reported experiments demonstrate better performance than some of the state-of-the-art methods. We report on a new distance measure between n-gram profiles, which shows superior performance compared to many other measures, including commonly used Euclidean distance.
URI: https://research.matf.bg.ac.rs/handle/123456789/500
ISSN: 0169-2607
DOI: 10.1016/j.cmpb.2005.11.007
Appears in Collections:Research outputs

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