An association rule based model for information extraction from protein sequence data
In this paper, a data mining technique for protein sequence pattern extraction is developed. Specifically, the aim is to explore the use of association rules as a basis to build successful secondary structure predictors, in a sequencestructure layer. No heuristic or biological infor mation is taken...
- Autores:
-
Becerra, David
Cantor Monroy, Giovanni Antonio
Niño, Luis Fernando
Gómez Perdomo, Jonatan
Bobadilla, Leonardo
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2008
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/24338
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/24338
http://bdigital.unal.edu.co/15375/
- Palabra clave:
- Data Mining
Secondary Structure Prediction
Association Rules.
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | In this paper, a data mining technique for protein sequence pattern extraction is developed. Specifically, the aim is to explore the use of association rules as a basis to build successful secondary structure predictors, in a sequencestructure layer. No heuristic or biological infor mation is taken into account in the present study and only the information given by the association rules is used as a basis for building a secondary structure predictor. This work gives some insights about secondary structure prediction features to be used in learning algorithms; this is expected to be useful to achieve substantial improvements of accuracy in protein secondary structure prediction. |
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