Evolving sequence patterns for prediction of sub-cellular locations of eukaryotic proteins
Pages 1135 - 1136
Abstract
A genetic algorithm (GA) is utilised to discover known and novel PROSITE-like sequence templates that can be used to classify the sub-cellular location of eukaryotic proteins. While traditional machine learning techniques present a black-box approach to this problem, the current method explicitly represents the discovered localisation motifs. A combined multi-class location classifier is presented and compared to other techniques based on genetic programming. Without consideration of additional structural information the presented method outperforms the alternative techniques.
References
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Heddad, A., Brameier, M. and MacCallum, R.M. Evolving Regular Expression-Based Sequence Classifiers for Protein Nuclear Localisation. Applications Of Evolutionary Computing: EvoWorkshops 2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, and EvoSTOC, Coimbra, Portugal, April 5-7, 2004; Proceedings.
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Henikoff, S. and Henikoff, J.G. Amino Acid Substitution Matrices from Protein Blocks. PNAS, 89 (22). 10915--10919.
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Index Terms
- Evolving sequence patterns for prediction of sub-cellular locations of eukaryotic proteins
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July 2008
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Published: 12 July 2008
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