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- W307353023 abstract "Choosing the right representation for a problem is impor- tant. In this article we introduce a linear genetic program- ming approach for motif discovery in protein families, and we also present a thorough comparison between our ap- proach and Koza-style genetic programming using ADFs. In a study of 45 protein families, we demonstrate that our algorithm, given equal processing resources and no prior knowledge in shaping of datasets, consistently generates mo- tifs that are of significantly better quality than those we found by using trees as representation. For several of the studied protein families we evolve motifs comparable to those found in Prosite, a manually curated database of protein motifs. Our linear genome gave better results than Koza-style ge- netic programming for 37 of 45 families. The difference is statistically significant for 24 of the families at the 99% con- fidence level." @default.
- W307353023 created "2016-06-24" @default.
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- W307353023 date "2005-01-01" @default.
- W307353023 modified "2023-09-27" @default.
- W307353023 title "Discovering Biological Motifs With Genetic Programming Comparing linear and tree-based representations for unaligned protein sequences" @default.
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