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Brian J. ROSS (2002)

by Brock University Dept ,  Brian J. Ross
New Generation Computing
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Abstract:

Stochastic regular motifs are evolved for protein sequences using genetic programming. The motif language, SRE-DNA, is a stochastic regular expression language suitable for denoting biosequences. Three restricted versions of SRE-DNA are used as target languages for evolved motifs. The genetic programming experiments are implemented in DCTGGP, which is a genetic programming system that uses logic--based attribute grammars to define the target language for evolved programs.

Citations

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30 A generalized profile syntax for biomolecular sequence motifs and its function in automatic sequence interpretation – Bucher, Bairoch - 1994
17 Logic-based genetic programming with definite clause translation grammars – Ross - 1999
12 Protein sequence motifs – Bork, Koonin - 1996
11 A probabilistic language formalism for stochastic discrete event systems – Garg, Kumar, et al. - 1999
8 Biopattern Discovery by Genetic Programming – Hu - 1998
8 Probabilistic Pattern Matching and the Evolution of Stochastic Regular Expressions – Ross - 2000
7 Classifying nucleic acid subsequences as introns or exons using genetic programming – Handley - 1995
4 Automated learning of a detector for the cores of α-helices in protein sequences via genetic programming – Handley - 1994
3 The Evaluation of a Stochastic Regular Motif Language for Protein Sequences – Ross - 2001
1 The Evolution of Stochastic Regular Motifs for Protein Sequences 33 20 – Koza, Bennett, et al. - 1998
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