An Effective Identification of Species from DNA Sequence: A Classification Technique by Integrating DM and ANN

Kumar, S Ganesh

doi:10.14569/ijacsa.2012.030817

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…The obtained CGR is divided into 64 cells where black dots in CGR represent the frequency of trinucleotides. The author divided the trinucleotide frequency by the total length of the DNA sequence and framed a 64-element vector which is further used as features to classify the eight categories of Eukaryotic organisms using ANN.Sathish Kumar S and Duraipandian N [15] introduced a classification technique for DNA sequences by integrating Data mining and Artificial Neural Networks which is based on Pattern mining. The authors generated dinucleotide and trinucleotide as patterns and calculated the frequency for both set of patterns.…”

Section: Related Workmentioning

confidence: 99%

A Novel Frequency Based Feature Extraction Technique for Classification of Corona Virus Genome and Discovery of COVID-19 Repeat Pattern

Muthulakshmi

Ganesan

2021

Braz. arch. biol. technol.

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Genome sequence regulates the life of all living organisms on earth. Genetic diseases cause genomic disorders and therefore early prediction of severe genetic diseases is quite possible by Genome sequence analysis. Genomic disorders refer to the mutation that is rearrangement of bases in the Genome of an organism. Genome sequence analysis and mutation identification can help to classify the diseased genome which can be accomplished using Machine Learning techniques. Feature Extraction plays a crucial role in classification as it is used to convert the Genome sequences into a set of quantitative values. In this article, we propose a novel feature extraction technique called Frequency based Feature Extraction Technique which extracts 120 features from genome sequences for classification. In the current scenario, COVID-19 is the pandemic disease and Corona virus is the source of this disease. So, in this research work, we tested the proposed feature extraction technique with 1000 samples of Genome sequences of Corona virus affected patients across the world. The extracted features were classified using both Machine Learning and Deep Learning techniques. From the results, it is evident that the proposed feature extraction technique performs well with Convolutional Neural Network classifier giving an accuracy of 97.96%. The proposed technique also helps to find the most repeat patterns in the genome sequences. It is discovered that the pattern "TTGTT" is the most repeat pattern in COVID-19 genome.

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Section: Related Workmentioning

confidence: 99%

A Novel Frequency Based Feature Extraction Technique for Classification of Corona Virus Genome and Discovery of COVID-19 Repeat Pattern

Muthulakshmi

Ganesan

2021

Braz. arch. biol. technol.

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“…It is accepted that numerous significant scientific and innovative attempts in the 21st century will be identified with the preparing and interpretation of the tremendous information that is at currently exposed from sequencing the genomes of numerous living organisms' forms, including people. The genomic signal is one of the tools for revealing large scale features of DNA [8].Genomic signals carry genomic information to all the processes that take place in an organism [11]. Genomic sign are the measurable occasions starting from DNA succession, mRNA sequence and protein.…”

Section: Introductionmentioning

confidence: 99%

Cancer Identification VIA Weighted Entropy Method and Decision Tree Method

Grover¹

2018

IJRASET

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DNA sequence classification is a challenging problem of central importance to genomic research. A number of algorithms have been developed for this problem and implemented as systems that are in widespread use today. In this article, we discuss two systems to predict cancer based on the signal processing of the DNA sequence. In the first method, a system is proposed to predict cancer from DNA sequence through an efficient concept of weighted entropy that takes both entropy and total correlation into consideration. Here, a measure, named as weighted entropy is used which captures the distribution and correlation information of a DNA sequence. In the second method, a decision tree based cancer prediction is proposed, where a split point measure is used namely weighted entropy for pruning the decision tree. For the analysis, we make use of DNA sequences obtained from National Centre for Biotechnology Information (NCBI). Evaluation metrics parameters of sensitivity, specificity and accuracy are found out and compared to the existing literature. Experimental results demonstrate that, this proposed scheme has achieved an accuracy of 90% in classifying DNA sequences.

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Infants Disease Prediction Architecture Using Artificial Neural Networks and Digital Image Processing

Thamaraiselvi

Kaliappan

2014

2014 World Congress on Computing and Communication Technologies

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An Effective Identification of Species from DNA Sequence: A Classification Technique by Integrating DM and ANN

Cited by 3 publications

References 31 publications

A Novel Frequency Based Feature Extraction Technique for Classification of Corona Virus Genome and Discovery of COVID-19 Repeat Pattern

A Novel Frequency Based Feature Extraction Technique for Classification of Corona Virus Genome and Discovery of COVID-19 Repeat Pattern

Cancer Identification VIA Weighted Entropy Method and Decision Tree Method

Infants Disease Prediction Architecture Using Artificial Neural Networks and Digital Image Processing

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