%0 Journal Article %1 ross:2002:ngc %A Ross, Brian J. %D 2002 %J New Generation Computing %K Computation Evolutionary Expressions, Genetic Grammatical Motifs, Programming, Protein Regular Stochastic algorithms, expressions, genetic motif, programming, protein, regular stochastic %N 2 %P 187--213 %T The Evolution of Stochastic Regular Motifs for Protein Sequences %U http://citeseer.ist.psu.edu/507503.html %V 20 %X 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 DCTG-GP, which is a genetic programming system that uses logic--based attribute grammars to define the target language for evolved programs. Earlier preliminary work tested SRE-DNA's viability as a representation language for aligned protein sequences. This work establishes that SRE-DNA is also suitable for evolving motifs for unaligned sets of sequences.

@article{ross:2002:ngc, 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 DCTG-GP, which is a genetic programming system that uses logic--based attribute grammars to define the target language for evolved programs. Earlier preliminary work tested SRE-DNA's viability as a representation language for aligned protein sequences. This work establishes that SRE-DNA is also suitable for evolving motifs for unaligned sets of sequences.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Ross, Brian J.}, biburl = {https://www.bibsonomy.org/bibtex/2feca510d6493f0a8fb54928ffda05a0d/brazovayeye}, interhash = {196fe5e05e6f8e7cfeb06d1d37a04f73}, intrahash = {feca510d6493f0a8fb54928ffda05a0d}, journal = {New Generation Computing}, keywords = {Computation Evolutionary Expressions, Genetic Grammatical Motifs, Programming, Protein Regular Stochastic algorithms, expressions, genetic motif, programming, protein, regular stochastic}, month = {February}, number = 2, pages = {187--213}, timestamp = {2008-06-19T17:50:41.000+0200}, title = {The Evolution of Stochastic Regular Motifs for Protein Sequences}, url = {http://citeseer.ist.psu.edu/507503.html}, volume = 20, year = 2002 }