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Prediction of the Pro-longevity or Anti-Longevity Effect of Caenorhabditis Elegans Genes Based on Bayesian Classification Methods

Freitas, Alex A. (2013) Prediction of the Pro-longevity or Anti-Longevity Effect of Caenorhabditis Elegans Genes Based on Bayesian Classification Methods. In: 2013 IEEE International Conference on Bioinformatics and Biomedicine. IEEE, pp. 373-380. E-ISBN 978-1-4799-1309-1. (doi:10.1109/BIBM.2013.6732521) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://dx.doi.org/10.1109/BIBM.2013.6732521

Abstract

The genetic mechanisms of ageing are mysterious and sophisticated issues that attract biologists' attention. With the help of data mining techniques, some findings relevant to the ageing problem can be revealed. This paper studies the performance of Bayesian network augmented naive Bayes classifier, naive Bayes classifier and proposed feature selection methods for naive Bayes on predicting a C. elegans gene's effect on the organism's longevity. The results show that due to the hierarchical structure of predictor attribute values (Gene Ontology terms), the Bayesian network augmented naive Bayes classifier performs better than the naive Bayes classifier, and the proposed feature selection methods for naive Bayes can effectively optimize the predictive performance of naive Bayes.

Item Type: Book section
DOI/Identification number: 10.1109/BIBM.2013.6732521
Uncontrolled keywords: data mining, machine learning, gene ontology, Bayesian classifiers, feature selection
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 03 Mar 2014 18:26 UTC
Last Modified: 25 Sep 2019 11:03 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/38535 (The current URI for this page, for reference purposes)
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