Cargando…
DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning
N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic performance such as protein interactions, conformation, stability in DNA as well as the regulation of gene expression same cell developmental and genomic imprinting. Some different 4mC site identifiers have...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465362/ https://www.ncbi.nlm.nih.gov/pubmed/32707969 http://dx.doi.org/10.3390/cells9081756 |
_version_ | 1783577571778101248 |
---|---|
author | Wahab, Abdul Mahmoudi, Omid Kim, Jeehong Chong, Kil To |
author_facet | Wahab, Abdul Mahmoudi, Omid Kim, Jeehong Chong, Kil To |
author_sort | Wahab, Abdul |
collection | PubMed |
description | N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic performance such as protein interactions, conformation, stability in DNA as well as the regulation of gene expression same cell developmental and genomic imprinting. Some different 4mC site identifiers have been proposed for various species. Herein, we proposed a computational model, DNC4mC-Deep, including six encoding techniques plus a deep learning model to predict 4mC sites in the genome of F. vesca, R. chinensis, and Cross-species dataset. It was demonstrated by the 10-fold cross-validation test to get superior performance. The DNC4mC-Deep obtained 0.829 and 0.929 of MCC on F. vesca and R. chinensis training dataset, respectively, and 0.814 on cross-species. This means the proposed method outperforms the state-of-the-art predictors at least 0.284 and 0.265 on F. vesca and R. chinensis training dataset in turn. Furthermore, the DNC4mC-Deep achieved 0.635 and 0.565 of MCC on F. vesca and R. chinensis independent dataset, respectively, and 0.562 on cross-species which shows it can achieve the best performance to predict 4mC sites as compared to the state-of-the-art predictor. |
format | Online Article Text |
id | pubmed-7465362 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74653622020-09-04 DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning Wahab, Abdul Mahmoudi, Omid Kim, Jeehong Chong, Kil To Cells Article N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic performance such as protein interactions, conformation, stability in DNA as well as the regulation of gene expression same cell developmental and genomic imprinting. Some different 4mC site identifiers have been proposed for various species. Herein, we proposed a computational model, DNC4mC-Deep, including six encoding techniques plus a deep learning model to predict 4mC sites in the genome of F. vesca, R. chinensis, and Cross-species dataset. It was demonstrated by the 10-fold cross-validation test to get superior performance. The DNC4mC-Deep obtained 0.829 and 0.929 of MCC on F. vesca and R. chinensis training dataset, respectively, and 0.814 on cross-species. This means the proposed method outperforms the state-of-the-art predictors at least 0.284 and 0.265 on F. vesca and R. chinensis training dataset in turn. Furthermore, the DNC4mC-Deep achieved 0.635 and 0.565 of MCC on F. vesca and R. chinensis independent dataset, respectively, and 0.562 on cross-species which shows it can achieve the best performance to predict 4mC sites as compared to the state-of-the-art predictor. MDPI 2020-07-22 /pmc/articles/PMC7465362/ /pubmed/32707969 http://dx.doi.org/10.3390/cells9081756 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wahab, Abdul Mahmoudi, Omid Kim, Jeehong Chong, Kil To DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning |
title | DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning |
title_full | DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning |
title_fullStr | DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning |
title_full_unstemmed | DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning |
title_short | DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning |
title_sort | dnc4mc-deep: identification and analysis of dna n4-methylcytosine sites based on different encoding schemes by using deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465362/ https://www.ncbi.nlm.nih.gov/pubmed/32707969 http://dx.doi.org/10.3390/cells9081756 |
work_keys_str_mv | AT wahababdul dnc4mcdeepidentificationandanalysisofdnan4methylcytosinesitesbasedondifferentencodingschemesbyusingdeeplearning AT mahmoudiomid dnc4mcdeepidentificationandanalysisofdnan4methylcytosinesitesbasedondifferentencodingschemesbyusingdeeplearning AT kimjeehong dnc4mcdeepidentificationandanalysisofdnan4methylcytosinesitesbasedondifferentencodingschemesbyusingdeeplearning AT chongkilto dnc4mcdeepidentificationandanalysisofdnan4methylcytosinesitesbasedondifferentencodingschemesbyusingdeeplearning |