Please use this identifier to cite or link to this item: http://hdl.handle.net/10198/4385
Title: SimSearch: A new variant of dynamic programming based on distance series for optimal and near-optimal similarity discovery in biological sequences
Author: Deusdado, Sérgio
Carvalho, Paulo
Keywords: Similarity discovery
Dynamic programming
Distance series
Issue Date: 2008
Publisher: Springer-Verlag
Citation: Deusdado, Sérgio; Carvalho, Paulo (2008) - SimSearch: A new variant of dynamic programming based on distance series for optimal and near-optimal similarity discovery in biological sequences. In Corchado, Juan M. 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics. Berlin: Springer-Verlag. p. 206-216. ISBN 978-3-540-85860-7. (Advances in Soft Computing; 49)
Abstract: In this paper, we propose SimSearch, an algorithm implementing a new variant of dynamic programming based on distance series for optimal and near-optimal similarity discovery in biological sequences. The initial phase of SimSearch is devoted to fulfil the binary similarity matrices by signalling the distances between occurrences of the same symbol. The scoring scheme is further applied, when analysed the maximal extension of the pattern. Employing bit parallelism to analyse the global similarity matrix’s upper triangle, the new methodology searches the sequence(s) for all the exact and approximate patterns in regular or reverse order. The algorithm accepts parameterization to work with greater seeds for near-optimal results. Performance tests show significant efficiency improvement over traditional optimal methods based on dynamic programming. Comparing the new algorithm’s efficiency against heuristic based methods, equalizing the required sensitivity, the proposed algorithm remains acceptable.
URI: http://hdl.handle.net/10198/4385
ISBN: 978-3-540-85860-7
Publisher Version: http://www.springerlink.com/content/t3701n3011675773/
Appears in Collections:CSE - Capítulos de Livros

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