Species identification based on approximate matching

Nagamma Patil, Durga Toshniwal, Kumkum Garg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationCompute 2011 - 4th Annual ACM Bangalore Conference
Publication statusPublished - 09-06-2011
Externally publishedYes
Event4th Annual ACM Bangalore Conference, Compute 2011 - Bangalore, India
Duration: 25-03-201126-03-2011


Conference4th Annual ACM Bangalore Conference, Compute 2011

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications


Dive into the research topics of 'Species identification based on approximate matching'. Together they form a unique fingerprint.

Cite this