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Genetic programming for protein related text classification
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Proceedings of the 11th Annual conference on Genetic and evolutionary computation table of contents
Montreal, Québec, Canada
SESSION: Track 10: genetic programming table of contents
Pages 1099-1106  
Year of Publication: 2009
ISBN:978-1-60558-325-9
Authors
Marc Segond  European Center for Soft Computing, Mieres, Spain
Cyril Fonlupt  Université du Littoral - Côte d'Opale, Calais, France
Denis Robilliard  Université du Littoral - Côte d'Opale, Calais, France
Sponsors
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Since the genomics revolution, bioinformatics has never been so popular. Many researchers have investigated with great success the use of evolutionary computation in bioinformatics [19] for example in the field of protein folding or determining genome sequences. In this paper, instead of using evolutionary computation as a way to provide new and innovative solutions to complex bioinformatics problems, we use genetic programming as a tool to evolve programs that are able to automatically classify research papers as dealing or not with a given protein. In a second part, we show that the attributes that are selected by the genetic programming evolved programs can be used efficiently for proteins classification.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Marc Segond: colleagues
Cyril Fonlupt: colleagues
Denis Robilliard: colleagues