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Title: A procedure for biological sensitive pattern matching in protein sequences
Authors: Mendez, J 
Falcón Martel, Antonio 
Lorenzo, Javier 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Pattern matching
Biological mattern analysis
Sequence alignments
Multidimensional acaling
SIMD processing
Issue Date: 2003
Journal: Lecture Notes in Computer Science 
Conference: 1st Iberian Conference on Pattern Recognition and Image Analysis 
Abstract: A Procedure for fast pattern matching in protein sequences is presented. It uses a biological metric, based on the substitution matrices as PAM or BLOSUM, to compute the matching. Biological sensitive pattern matching does pattern detection according to the available empirical data about similarity and affinity relations between amino acids in protein sequences. Sequence alignments is a string matching procedure used in Genomic; it includes insert/delete operators and dynamic programming techniques; it provides more sophisticate results that other pattern matching procedures but with higher computational cost. Heuristic procedures for local alignments as FASTA or BLAST are used to reduce this cost. They are based on some successive tasks; the first one uses a pattern matching procedure with very short sequences, also named k-tuples. This paper shows how using the L 1 metric this matching task can be efficiently computed by using SIMD instructions. To design this procedure, a table that maps the substitution matrices is needed. This table defines a representation of each amino acid residue in a n-dimensional space of lower dimensionality as possible; this is accomplished by using techniques of Multidimensional Scaling used in Pattern Recognition and Machine Learning for dimensionality reduction. Based on the experimental tests, the proposed procedure provides a favorable ration of cost vs matching quality.
ISBN: 978-3-540-40217-6
ISSN: 0302-9743
DOI: 10.1007/978-3-540-44871-6_64
Source: Perales F.J., Campilho A.J.C., de la Blanca N.P., Sanfeliu A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652, p. 547-555. Springer, Berlin, Heidelberg.
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