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Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA

Over the last decade, the field of bioinformatics has been increasing rapidly. Robust bioinformatics tools are going to play a vital role in future progress. Scientists working in the field of bioinformatics conduct a large number of researches to extract knowledge from the biological data available...

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Autores principales: R., Elakkiya, Jain, Deepak Kumar, Kotecha, Ketan, Pandya, Sharnil, Reddy, Sai Siddhartha, E., Rajalakshmi, Varadarajan, Vijayakumar, Mahanti, Aniket, V, Subramaniyaswamy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733243/
https://www.ncbi.nlm.nih.gov/pubmed/35004605
http://dx.doi.org/10.3389/fpubh.2021.821410
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author R., Elakkiya
Jain, Deepak Kumar
Kotecha, Ketan
Pandya, Sharnil
Reddy, Sai Siddhartha
E., Rajalakshmi
Varadarajan, Vijayakumar
Mahanti, Aniket
V, Subramaniyaswamy
author_facet R., Elakkiya
Jain, Deepak Kumar
Kotecha, Ketan
Pandya, Sharnil
Reddy, Sai Siddhartha
E., Rajalakshmi
Varadarajan, Vijayakumar
Mahanti, Aniket
V, Subramaniyaswamy
author_sort R., Elakkiya
collection PubMed
description Over the last decade, the field of bioinformatics has been increasing rapidly. Robust bioinformatics tools are going to play a vital role in future progress. Scientists working in the field of bioinformatics conduct a large number of researches to extract knowledge from the biological data available. Several bioinformatics issues have evolved as a result of the creation of massive amounts of unbalanced data. The classification of precursor microRNA (pre miRNA) from the imbalanced RNA genome data is one such problem. The examinations proved that pre miRNAs (precursor microRNAs) could serve as oncogene or tumor suppressors in various cancer types. This paper introduces a Hybrid Deep Neural Network framework (H-DNN) for the classification of pre miRNA in imbalanced data. The proposed H-DNN framework is an integration of Deep Artificial Neural Networks (Deep ANN) and Deep Decision Tree Classifiers. The Deep ANN in the proposed H-DNN helps to extract the meaningful features and the Deep Decision Tree Classifier helps to classify the pre miRNA accurately. Experimentation of H-DNN was done with genomes of animals, plants, humans, and Arabidopsis with an imbalance ratio up to 1:5000 and virus with a ratio of 1:400. Experimental results showed an accuracy of more than 99% in all the cases and the time complexity of the proposed H-DNN is also very less when compared with the other existing approaches.
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spelling pubmed-87332432022-01-07 Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA R., Elakkiya Jain, Deepak Kumar Kotecha, Ketan Pandya, Sharnil Reddy, Sai Siddhartha E., Rajalakshmi Varadarajan, Vijayakumar Mahanti, Aniket V, Subramaniyaswamy Front Public Health Public Health Over the last decade, the field of bioinformatics has been increasing rapidly. Robust bioinformatics tools are going to play a vital role in future progress. Scientists working in the field of bioinformatics conduct a large number of researches to extract knowledge from the biological data available. Several bioinformatics issues have evolved as a result of the creation of massive amounts of unbalanced data. The classification of precursor microRNA (pre miRNA) from the imbalanced RNA genome data is one such problem. The examinations proved that pre miRNAs (precursor microRNAs) could serve as oncogene or tumor suppressors in various cancer types. This paper introduces a Hybrid Deep Neural Network framework (H-DNN) for the classification of pre miRNA in imbalanced data. The proposed H-DNN framework is an integration of Deep Artificial Neural Networks (Deep ANN) and Deep Decision Tree Classifiers. The Deep ANN in the proposed H-DNN helps to extract the meaningful features and the Deep Decision Tree Classifier helps to classify the pre miRNA accurately. Experimentation of H-DNN was done with genomes of animals, plants, humans, and Arabidopsis with an imbalance ratio up to 1:5000 and virus with a ratio of 1:400. Experimental results showed an accuracy of more than 99% in all the cases and the time complexity of the proposed H-DNN is also very less when compared with the other existing approaches. Frontiers Media S.A. 2021-12-23 /pmc/articles/PMC8733243/ /pubmed/35004605 http://dx.doi.org/10.3389/fpubh.2021.821410 Text en Copyright © 2021 R., Jain, Kotecha, Pandya, Reddy, E., Varadarajan, Mahanti and V. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
R., Elakkiya
Jain, Deepak Kumar
Kotecha, Ketan
Pandya, Sharnil
Reddy, Sai Siddhartha
E., Rajalakshmi
Varadarajan, Vijayakumar
Mahanti, Aniket
V, Subramaniyaswamy
Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA
title Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA
title_full Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA
title_fullStr Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA
title_full_unstemmed Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA
title_short Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA
title_sort hybrid deep neural network for handling data imbalance in precursor microrna
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733243/
https://www.ncbi.nlm.nih.gov/pubmed/35004605
http://dx.doi.org/10.3389/fpubh.2021.821410
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