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Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach
Meiotic recombination is the driving force of evolutionary development and an important source of genetic variation. The meiotic recombination does not take place randomly in a chromosome but occurs in some regions of the chromosome. A region in chromosomes with higher rate of meiotic recombination...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7527634/ https://www.ncbi.nlm.nih.gov/pubmed/33093842 http://dx.doi.org/10.3389/fgene.2020.539227 |
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author | Khan, Fatima Khan, Mukhtaj Iqbal, Nadeem Khan, Salman Muhammad Khan, Dost Khan, Abbas Wei, Dong-Qing |
author_facet | Khan, Fatima Khan, Mukhtaj Iqbal, Nadeem Khan, Salman Muhammad Khan, Dost Khan, Abbas Wei, Dong-Qing |
author_sort | Khan, Fatima |
collection | PubMed |
description | Meiotic recombination is the driving force of evolutionary development and an important source of genetic variation. The meiotic recombination does not take place randomly in a chromosome but occurs in some regions of the chromosome. A region in chromosomes with higher rate of meiotic recombination events are considered as hotspots and a region where frequencies of the recombination events are lower are called coldspots. Prediction of meiotic recombination spots provides useful information about the basic functionality of inheritance and genome diversity. This study proposes an intelligent computational predictor called iRSpots-DNN for the identification of recombination spots. The proposed predictor is based on a novel feature extraction method and an optimized deep neural network (DNN). The DNN was employed as a classification engine whereas, the novel features extraction method was developed to extract meaningful features for the identification of hotspots and coldspots across the yeast genome. Unlike previous algorithms, the proposed feature extraction avoids bias among different selected features and preserved the sequence discriminant properties along with the sequence-structure information simultaneously. This study also considered other effective classifiers named support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF) to predict recombination spots. Experimental results on a benchmark dataset with 10-fold cross-validation showed that iRSpots-DNN achieved the highest accuracy, i.e., 95.81%. Additionally, the performance of the proposed iRSpots-DNN is significantly better than the existing predictors on a benchmark dataset. The relevant benchmark dataset and source code are freely available at: https://github.com/Fatima-Khan12/iRspot_DNN/tree/master/iRspot_DNN. |
format | Online Article Text |
id | pubmed-7527634 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-75276342020-10-21 Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach Khan, Fatima Khan, Mukhtaj Iqbal, Nadeem Khan, Salman Muhammad Khan, Dost Khan, Abbas Wei, Dong-Qing Front Genet Genetics Meiotic recombination is the driving force of evolutionary development and an important source of genetic variation. The meiotic recombination does not take place randomly in a chromosome but occurs in some regions of the chromosome. A region in chromosomes with higher rate of meiotic recombination events are considered as hotspots and a region where frequencies of the recombination events are lower are called coldspots. Prediction of meiotic recombination spots provides useful information about the basic functionality of inheritance and genome diversity. This study proposes an intelligent computational predictor called iRSpots-DNN for the identification of recombination spots. The proposed predictor is based on a novel feature extraction method and an optimized deep neural network (DNN). The DNN was employed as a classification engine whereas, the novel features extraction method was developed to extract meaningful features for the identification of hotspots and coldspots across the yeast genome. Unlike previous algorithms, the proposed feature extraction avoids bias among different selected features and preserved the sequence discriminant properties along with the sequence-structure information simultaneously. This study also considered other effective classifiers named support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF) to predict recombination spots. Experimental results on a benchmark dataset with 10-fold cross-validation showed that iRSpots-DNN achieved the highest accuracy, i.e., 95.81%. Additionally, the performance of the proposed iRSpots-DNN is significantly better than the existing predictors on a benchmark dataset. The relevant benchmark dataset and source code are freely available at: https://github.com/Fatima-Khan12/iRspot_DNN/tree/master/iRspot_DNN. Frontiers Media S.A. 2020-09-17 /pmc/articles/PMC7527634/ /pubmed/33093842 http://dx.doi.org/10.3389/fgene.2020.539227 Text en Copyright © 2020 Khan, Khan, Iqbal, Khan, Muhammad Khan, Khan and Wei. http://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 | Genetics Khan, Fatima Khan, Mukhtaj Iqbal, Nadeem Khan, Salman Muhammad Khan, Dost Khan, Abbas Wei, Dong-Qing Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach |
title | Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach |
title_full | Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach |
title_fullStr | Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach |
title_full_unstemmed | Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach |
title_short | Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach |
title_sort | prediction of recombination spots using novel hybrid feature extraction method via deep learning approach |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7527634/ https://www.ncbi.nlm.nih.gov/pubmed/33093842 http://dx.doi.org/10.3389/fgene.2020.539227 |
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