Analysis of parametric images derived from genomic sequences using neural network based approaches

Giron, Alain; Vilain, Joseph; Serruys, Camille; Brahmi, Djamel; Deschavanne, Patrick; Fertil, Bernard

doi:10.1109/ijcnn.1999.836252

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…The possibility of species classification using CGR images of DNA sequences, by using different distance metrics [28] and by using neural networks [29] has been investigated. In both these works, only species identification is done taking a few different species.…”

Section: Related Workmentioning

confidence: 99%

Genome data classification based on fuzzy matching

Patil

Toshniwal

Garg

2012

CSIT

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Genomic data mining and knowledge extraction is an important problem in bioinformatics. Some research work has been done on unknown genome identification and is based on exact pattern matching of n-grams. In most of the real world biological problems exact matching may not give desired results and the problem in using n-grams is exponential explosion. In this paper we propose a method for genome data classification based on approximate matching. The algorithm works by selecting random samples from the genome database. Tolerance is allowed by generating candidates of varied length to query from these sample sequences. The Levenshtein distance is then checked for each candidate and whether they are k-fuzzily equal. The total number of fuzzy matches for each sequence is then calculated. This is then classified using the data mining techniques namely, naive Bayes, support vector machine, back propagation and also by nearest neighbor. Experiment results are provided for different tolerance levels and they show that accuracy increases as tolerance does. 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 Yeast and E. coli are used to verify proposed method.

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Section: Related Workmentioning

confidence: 99%

Genome data classification based on fuzzy matching

Patil

Toshniwal

Garg

2012

CSIT

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“…Possibility of species classification using CGR images of DNA sequences, by using different distance metrics [7] and by using neural networks [8] has been investigated. A detailed classification problem, using a combination of FCGR and Naive Bayesian approach is explained in Vrinda et al [9].…”

Section: Introductionmentioning

confidence: 99%

ANN based Genome Classifier using Frequency Chaos Game Representation

Nair¹,

Vijayan²,

Gopinath³

et al. 2010

IJET

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Abstract-Classificationof organisms into different categories using their genomic sequences has found its importance in the study of evolutionary characteristics of organisms and specific identification of previously unknown organisms in bio-diversity studies and related areas. Chaos game representation (CGR) uniquely represents DNA sequence in a visual format 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 from NCBI and ANN is used for classification. Different con-figurations of ANN are tested and good accuracy is obtained. The way, the fractal nature of CGR helps in classification, is also investigated.

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Bio-sequence Signatures Using Chaos Game Representation

Nair

Arun

et al. 2009

Bioinformatics: Applications in Life and Environmental Sciences

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Analysis of parametric images derived from genomic sequences using neural network based approaches

Cited by 7 publications

References 3 publications

Genome data classification based on fuzzy matching

Genome data classification based on fuzzy matching

ANN based Genome Classifier using Frequency Chaos Game Representation

Bio-sequence Signatures Using Chaos Game Representation

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