DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species

DNA N4-methylcytosine (4mC) being a significant genetic modification holds a dominant role in controlling different biological functions, i.e., DNA replication, DNA repair, gene regulations and gene expression levels. The identification of 4mC sites is important to get insight information regarding...

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Autores principales:	Mobeen Ur Rehman, Hilal Tayara, Kil To Chong
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Bioinformatics 4mC modification Computational Biology Convolutional neural network Deep learning Biotechnology TP248.13-248.65
Acceso en línea:	https://doaj.org/article/9b817e5ac67246b9bc2cd291455b296e
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spelling	oai:doaj.org-article:9b817e5ac67246b9bc2cd291455b296e2021-11-18T04:46:28ZDCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species2001-037010.1016/j.csbj.2021.10.034https://doaj.org/article/9b817e5ac67246b9bc2cd291455b296e2021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2001037021004566https://doaj.org/toc/2001-0370DNA N4-methylcytosine (4mC) being a significant genetic modification holds a dominant role in controlling different biological functions, i.e., DNA replication, DNA repair, gene regulations and gene expression levels. The identification of 4mC sites is important to get insight information regarding different organics mechanisms. However, getting modification prediction from experimental methods is a challenging task due to high expenses and time-consuming techniques. Therefore, computational tools can be a great option for modification identification. Various computational tools are proposed in literature but their generalization and prediction performance require improvement. For this motive, we have proposed a neural network based tool named DCNN-4mC for identifying 4mC sites. The proposed model involves a set of neural network layers with a skip connection which allows to share the shallow features with dense layers. Skip connection have allowed to gather crucial information regarding 4mC sites. In literature, different models are employed on different species hence in many cases different datasets are available for a single species. In this research, we have combined all available datasets to create a single benchmark dataset for every species. To the best of our knowledge, no model in literature is employed on more than six different species. To ensure the generalizability of DCNN-4mC we have used 12 different species for performance evaluation. The DCNN-4mC tool has attained 2% to 14% higher accuracy than state-of-the-art tools on all available datasets of different species. Furthermore, independent test datasets are also engaged and DCNN-4mC have overall yielded high performance in them as well.Mobeen Ur RehmanHilal TayaraKil To ChongElsevierarticleBioinformatics4mC modificationComputational BiologyConvolutional neural networkDeep learningBiotechnologyTP248.13-248.65ENComputational and Structural Biotechnology Journal, Vol 19, Iss , Pp 6009-6019 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bioinformatics 4mC modification Computational Biology Convolutional neural network Deep learning Biotechnology TP248.13-248.65
spellingShingle	Bioinformatics 4mC modification Computational Biology Convolutional neural network Deep learning Biotechnology TP248.13-248.65 Mobeen Ur Rehman Hilal Tayara Kil To Chong DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species
description	DNA N4-methylcytosine (4mC) being a significant genetic modification holds a dominant role in controlling different biological functions, i.e., DNA replication, DNA repair, gene regulations and gene expression levels. The identification of 4mC sites is important to get insight information regarding different organics mechanisms. However, getting modification prediction from experimental methods is a challenging task due to high expenses and time-consuming techniques. Therefore, computational tools can be a great option for modification identification. Various computational tools are proposed in literature but their generalization and prediction performance require improvement. For this motive, we have proposed a neural network based tool named DCNN-4mC for identifying 4mC sites. The proposed model involves a set of neural network layers with a skip connection which allows to share the shallow features with dense layers. Skip connection have allowed to gather crucial information regarding 4mC sites. In literature, different models are employed on different species hence in many cases different datasets are available for a single species. In this research, we have combined all available datasets to create a single benchmark dataset for every species. To the best of our knowledge, no model in literature is employed on more than six different species. To ensure the generalizability of DCNN-4mC we have used 12 different species for performance evaluation. The DCNN-4mC tool has attained 2% to 14% higher accuracy than state-of-the-art tools on all available datasets of different species. Furthermore, independent test datasets are also engaged and DCNN-4mC have overall yielded high performance in them as well.
format	article
author	Mobeen Ur Rehman Hilal Tayara Kil To Chong
author_facet	Mobeen Ur Rehman Hilal Tayara Kil To Chong
author_sort	Mobeen Ur Rehman
title	DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species
title_short	DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species
title_full	DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species
title_fullStr	DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species
title_full_unstemmed	DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species
title_sort	dcnn-4mc: densely connected neural network based n4-methylcytosine site prediction in multiple species
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/9b817e5ac67246b9bc2cd291455b296e
work_keys_str_mv	AT mobeenurrehman dcnn4mcdenselyconnectedneuralnetworkbasedn4methylcytosinesitepredictioninmultiplespecies AT hilaltayara dcnn4mcdenselyconnectedneuralnetworkbasedn4methylcytosinesitepredictioninmultiplespecies AT kiltochong dcnn4mcdenselyconnectedneuralnetworkbasedn4methylcytosinesitepredictioninmultiplespecies
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DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species

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