Prediction of the pro-longevity or anti-longevity effect of Caenorhabditis Elegans genes based on Bayesian classification methods

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.