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AUTOMATING RECOGNITION OF REGIONS OF INTRINSICALLY POOR MULTIPLE ALIGNMENT FOR PHYLOGENETIC ANALYSIS USING MACHINE LEARNING

Phylogenetic analysis requires alignment of gene sequences. Automatic alignment programs produced regions of intrinsically poor alignment that are currently detected and deleted manually. We present the results of a machine learning approach to detection of these regions of the alignment. We compare naive Bayes, standard decision trees, and support vector machines. The results show three algorithms can accurately identify the bad sites of multiple sequence alignment based on three attributes such as the gap ratio, the site likelihood and the degree of homoplasy (consistency index). Among three algorithms, naive Bayes and SVM provide the best performance for bad site prediction, but C4.5 decision trees provide the best performance for the ambiguous and good site predictions. Among three classes, it is the most difficult to distinguish the ambiguous sites and the good sites, but easiest to distinguish the bad sites among these three classes. No evident difference among three parsimony count index as an attribute for reducing classification error is observed. Generally, the classifiers of naive Bayes and C4.5 decision tree learnt from the subset of a balanced class distribution generally come up with optimal performance compared with natural class distributions: the random subset and the entire data set.