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Analysis of DNA Sequence Classification Using CNN and Hybrid Models.
Gunasekaran, Hemalatha; Ramalakshmi, K; Rex Macedo Arokiaraj, A; Deepa Kanmani, S; Venkatesan, Chandran; Suresh Gnana Dhas, C.
  • Gunasekaran H; IT Department, University of Technology and Applied Sciences, Oman.
  • Ramalakshmi K; Department of Computer Science and Engineering, Alliance School of Engineering and Design, Alliance University, Bangalore, Karnataka, India.
  • Rex Macedo Arokiaraj A; IT Department, University of Technology and Applied Sciences, Oman.
  • Deepa Kanmani S; Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
  • Venkatesan C; Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India.
  • Suresh Gnana Dhas C; Department of Computer Science, Ambo University, Ambo, Post Box No.: 19, Ethiopia.
Comput Math Methods Med ; 2021: 1835056, 2021.
Article in English | MEDLINE | ID: covidwho-1315820
ABSTRACT
In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days, deep learning (DL) models can automatically extract the features from the input. In this work, we employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and K-mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with K-mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Neural Networks, Computer / Sequence Analysis, DNA / High-Throughput Nucleotide Sequencing / SARS-CoV-2 / COVID-19 Type of study: Experimental Studies Limits: Humans Language: English Journal: Comput Math Methods Med Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 2021

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Neural Networks, Computer / Sequence Analysis, DNA / High-Throughput Nucleotide Sequencing / SARS-CoV-2 / COVID-19 Type of study: Experimental Studies Limits: Humans Language: English Journal: Comput Math Methods Med Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 2021