species identification based on approximate matching
Genomic data mining and knowledge extraction is an important problem in bioinformatics. Existing methods for species identification are based on n -grams. In this paper, we propose a novel approach for identification of species. Given a database of genomic sequences, our proposed work includes extraction of all candidate/subsequences that satisfy: length grater or equal to given minimum length, given number of mismatches and support grater or equal to user threshold. These patterns are used as features for classifier. Classification of genome sequences has been done by using data mining techniques namely, Naive Bayes, support vector machine and nearest neighbor. Individual classifier accuracies are reported. We also show the effect of sampling size on the classification accuracy and it was observed that classification accuracy increases with sampling size. Genome data of two species namely E. coli and Yeast are used to verify proposed method.
genome data data mining bioinformatics soft computing exact matching approximate pattern matching