iEnhancer-MFGBDT: Identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree

Enhancer is a non-coding DNA fragment that can be bound with proteins to activate transcription of a gene, hence play an important role in regulating gene expression. Enhancer identification is very challenging and more complicated than other genetic factors due to their position variation and free...

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Autores principales:	Yunyun Liang, Shengli Zhang, Huijuan Qiao, Yinan Cheng
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	identification enhancers multiple features gradient boosting decision tree Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/16255e1ecfdb4bcc9d4ab534177f7a41
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id	oai:doaj.org-article:16255e1ecfdb4bcc9d4ab534177f7a41
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spelling	oai:doaj.org-article:16255e1ecfdb4bcc9d4ab534177f7a412021-11-29T02:37:31ZiEnhancer-MFGBDT: Identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree10.3934/mbe.20214341551-0018https://doaj.org/article/16255e1ecfdb4bcc9d4ab534177f7a412021-10-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021434?viewType=HTMLhttps://doaj.org/toc/1551-0018Enhancer is a non-coding DNA fragment that can be bound with proteins to activate transcription of a gene, hence play an important role in regulating gene expression. Enhancer identification is very challenging and more complicated than other genetic factors due to their position variation and free scattering. In addition, it has been proved that genetic variation in enhancers is related to human diseases. Therefore, identification of enhancers and their strength has important biological meaning. In this paper, a novel model named iEnhancer-MFGBDT is developed to identify enhancer and their strength by fusing multiple features and gradient boosting decision tree (GBDT). Multiple features include k-mer and reverse complement k-mer nucleotide composition based on DNA sequence, and second-order moving average, normalized Moreau-Broto auto-cross correlation and Moran auto-cross correlation based on dinucleotide physical structural property matrix. Then we use GBDT to select features and perform classification successively. The accuracies reach 78.67% and 66.04% for identifying enhancers and their strength on the benchmark dataset, respectively. Compared with other models, the results show that our model is useful and effective intelligent tool to identify enhancers and their strength, of which the datasets and source codes are available at https://github.com/shengli0201/iEnhancer-MFGBDT1.Yunyun LiangShengli ZhangHuijuan QiaoYinan ChengAIMS Pressarticleidentificationenhancersmultiple featuresgradient boosting decision treeBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 8797-8814 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	identification enhancers multiple features gradient boosting decision tree Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	identification enhancers multiple features gradient boosting decision tree Biotechnology TP248.13-248.65 Mathematics QA1-939 Yunyun Liang Shengli Zhang Huijuan Qiao Yinan Cheng iEnhancer-MFGBDT: Identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree
description	Enhancer is a non-coding DNA fragment that can be bound with proteins to activate transcription of a gene, hence play an important role in regulating gene expression. Enhancer identification is very challenging and more complicated than other genetic factors due to their position variation and free scattering. In addition, it has been proved that genetic variation in enhancers is related to human diseases. Therefore, identification of enhancers and their strength has important biological meaning. In this paper, a novel model named iEnhancer-MFGBDT is developed to identify enhancer and their strength by fusing multiple features and gradient boosting decision tree (GBDT). Multiple features include k-mer and reverse complement k-mer nucleotide composition based on DNA sequence, and second-order moving average, normalized Moreau-Broto auto-cross correlation and Moran auto-cross correlation based on dinucleotide physical structural property matrix. Then we use GBDT to select features and perform classification successively. The accuracies reach 78.67% and 66.04% for identifying enhancers and their strength on the benchmark dataset, respectively. Compared with other models, the results show that our model is useful and effective intelligent tool to identify enhancers and their strength, of which the datasets and source codes are available at https://github.com/shengli0201/iEnhancer-MFGBDT1.
format	article
author	Yunyun Liang Shengli Zhang Huijuan Qiao Yinan Cheng
author_facet	Yunyun Liang Shengli Zhang Huijuan Qiao Yinan Cheng
author_sort	Yunyun Liang
title	iEnhancer-MFGBDT: Identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree
title_short	iEnhancer-MFGBDT: Identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree
title_full	iEnhancer-MFGBDT: Identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree
title_fullStr	iEnhancer-MFGBDT: Identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree
title_full_unstemmed	iEnhancer-MFGBDT: Identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree
title_sort	ienhancer-mfgbdt: identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/16255e1ecfdb4bcc9d4ab534177f7a41
work_keys_str_mv	AT yunyunliang ienhancermfgbdtidentifyingenhancersandtheirstrengthbyfusingmultiplefeaturesandgradientboostingdecisiontree AT shenglizhang ienhancermfgbdtidentifyingenhancersandtheirstrengthbyfusingmultiplefeaturesandgradientboostingdecisiontree AT huijuanqiao ienhancermfgbdtidentifyingenhancersandtheirstrengthbyfusingmultiplefeaturesandgradientboostingdecisiontree AT yinancheng ienhancermfgbdtidentifyingenhancersandtheirstrengthbyfusingmultiplefeaturesandgradientboostingdecisiontree
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iEnhancer-MFGBDT: Identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree

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