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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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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. |
format | Online Article Text |
id | pubmed-8733243 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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