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Deterministic Pattern Mining On Genetic Sequences

Deterministic Pattern Mining On Genetic Sequences

Pedro Gabriel Ferreira, Paulo Jorge Azevedo
Copyright: © 2010 |Pages: 25
ISBN13: 9781605667669|ISBN10: 1605667668|EISBN13: 9781605667676
DOI: 10.4018/978-1-60566-766-9.ch013
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MLA

Ferreira, Pedro Gabriel, and Paulo Jorge Azevedo. "Deterministic Pattern Mining On Genetic Sequences." Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, edited by Emilio Soria Olivas, et al., IGI Global, 2010, pp. 277-301. https://doi.org/10.4018/978-1-60566-766-9.ch013

APA

Ferreira, P. G. & Azevedo, P. J. (2010). Deterministic Pattern Mining On Genetic Sequences. In E. Olivas, J. Guerrero, M. Martinez-Sober, J. Magdalena-Benedito, & A. Serrano López (Eds.), Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques (pp. 277-301). IGI Global. https://doi.org/10.4018/978-1-60566-766-9.ch013

Chicago

Ferreira, Pedro Gabriel, and Paulo Jorge Azevedo. "Deterministic Pattern Mining On Genetic Sequences." In Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, edited by Emilio Soria Olivas, et al., 277-301. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-766-9.ch013

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Abstract

The recent increase in the number of complete genetic sequences freely available through specialized Internet databases presents big challenges for the research community. One such challenge is the efficient and effective search of sequence patterns, also known as motifs, among a set of related genetic sequences. Such patterns describe regions that may provide important insights about the structural and functional role of DNA and proteins. Two main classes can be considered: probabilistic patterns represent a model that simulates the sequences or part of the sequences under consideration and deterministic patterns that either match or not the input sequences. In this chapter a general overview of deterministic sequence mining over sets of genetic sequences is proposed. The authors formulate an architecture that divides the mining process workflow into a set of blocks. Each of these blocks is discussed individually.

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