Authors:
Carlos M. Travieso
1
;
Jordi Solé-Casals
2
;
Jesús B. Alonso
1
and
Miguel A. Ferrer
1
Affiliations:
1
Technological Centre for Innovation in Communnications, University of Las Palmas de Gran Canaria, Spain
;
2
Digital Technologies Group, University of Vic, Spain
Keyword(s):
Feature reduction, Principal Component Analysis, Independent Components Analysis, Classification system, Deoxyribonucleic acid (DNA).
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
This paper has implemented a feature reduction based on Independent Components Analysis (ICA) and Principal Component Analysis (PCA) for an automatic supervised identification system of Pejibaye palm DNA markers, using an Artificial Neural Network (ANN) as classifier; obtaining 100% for the classes’ identification. The biochemical parameterization proposed, based on 89 RAPD primer markers applied on haplotypes of Pejibaye races, has correctly been proved for its reduction. The computational times have been studied, obtaining results in real time for test mode. Finally the interesting combination of these techniques (biochemical and computational), gives rise to a formulation of an inexpensive and handy method of origin denomination plant certification.