Protein Motif Discovery with Linear Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{conf/kes/Seehuus05,

• title = "Protein Motif Discovery with Linear Genetic Programming",
• author = "Rolv Seehuus",
• year = "2005",
• booktitle = "Knowledge-Based Intelligent Information and Engineering Systems: 9th International Conference, KES 2005, Proceedings, Part III",
• editor = "Rajiv Khosla and Robert J. Howlett and Lakhmi C. Jain",
• volume = "3683",
• series = "Lecture Notes in Computer Science",
• pages = "770--776",
• address = "Melbourne, Australia",
• publisher_address = "Berlin / Heidelberg",
• month = sep # " 14-16",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, linear genetic programming, ListGP",
• bibdate = "2005-09-05",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/kes/kes2005-3.html#Seehuus05",
• ISBN = "3-540-28896-1",
• DOI = "doi:10.1007/11553939_109",
• size = "7 pages",
• abstract = "There have been published some studies of genetic programming as a way to discover motifs in proteins and other biological data. These studies have been small, and often used domain knowledge to improve search. In this paper we present a genetic programming algorithm, that does not use domain knowledge, with results on 44 different protein families. We demonstrate that our list-based representation, given a fixed amount of processing resources, is able to discover meaningful motifs with good classification performance. Sometimes comparable to or even surpassing that of motifs found in a database of manually created motifs. We also investigate introduction of gaps in our algorithm, and it seems that this give a small increase in classification accuracy and recall, but with reduced precision.",
• notes = "hardware search speed up chip, PMC. regular expressions. Max 64 residues, no grammar?, wildcards, flexible gaps",

}

Genetic Programming entries for Rolv Seehuus

Citations