Paper Info
Title
An association rule based approach for biological sequence feature classification
Abstract
In this paper, an extraction and classification feature approach of biological sequences based on profiles built using an association analysis is proposed. The most important features of the approach are: i) The use of data mining techniques to perform knowledge extraction from biological sequences. Specifically an association analysis process is proposed as a methodology for discovering interesting relationships hidden in biological data sets; and ii) Some learning classifiers are proposed to be trained using binary profiles obtained from the association analysis process. These learning methods were applied over a sequence structure layer of secondary structure predictors to analyze the performance of association rules as a pattern extraction method. Some experiments were carried out to validate the proposed approach obtaining very promising results.
Year
DOI
Venue
2009
10.1109/CEC.2009.4983337
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
data mining,intelligent systems,association rule,biology,proteins,learning artificial intelligence,association analysis,support vector machines,machine learning,secondary structure,association rules,amino acids,feature extraction,pattern analysis,molecular biophysics,knowledge extraction,biological data
Data mining,Biological data,Sequence Feature,Pattern recognition,Computer science,Support vector machine,Feature extraction,Association rule learning,Knowledge extraction,Artificial intelligence,Machine learning,Binary number
Conference
Citations 
PageRank 
References 
0
0.34
2
Authors
4
Name
Order
Citations
PageRank
David Becerra141.43
Diana Vanegas240.76
Giovanni Cantor321.48
Luis Fernando Niño Vasquez410.72