ANN Based Classification of Unknown Genome Fragments Using Chaos Game Representation

Classification of organisms into different categories using their genomic sequences has found importance in study of evolutionary characteristics, specific identification of previously unknown organisms, study of mutual relationships between organisms and many other aspects in the study of living th...

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Bibliographic Details
Main Authors: Nair, Vrinda V, Vijayan, Karthika, Gopinath, Deepa P, Nair, Achuthsankar S
Format: Conference Proceeding
Language:English
Subjects:
ANN
Artificial neural networks
Bioinformatics
CGR
Chaos
DNA
DNA sequence
FCGR
Fractals
Frequency
Genomics
Organisms
Sequences
Testing
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Summary:Classification of organisms into different categories using their genomic sequences has found importance in study of evolutionary characteristics, specific identification of previously unknown organisms, study of mutual relationships between organisms and many other aspects in the study of living things. Chaos game representation (CGR) uniquely represents DNA sequences and reveals hidden patterns in it. Frequency-CGR (FCGR) derived from CGR, shows the frequency of sub-sequences present in the DNA sequence. In this paper, a novel method for classification of organisms based on a combination of FCGR and Artificial Neural network (ANN) is proposed. Eight categories from the taxonomical distribution of Eukaryotic organisms are taken and ANN is used for classification. Different configurations of ANN are tested and good accuracy is obtained. The way the fractal nature of DNA helps in classification, is also investigated.
DOI:10.1109/ICMLC.2010.56