Literature DB >> 30052767

4mCPred: machine learning methods for DNA N4-methylcytosine sites prediction.

Wenying He1, Cangzhi Jia2, Quan Zou1.   

Abstract

MOTIVATION: N4-methylcytosine (4mC), an important epigenetic modification formed by the action of specific methyltransferases, plays an essential role in DNA repair, expression and replication. The accurate identification of 4mC sites aids in-depth research to biological functions and mechanisms. Because, experimental identification of 4mC sites is time-consuming and costly, especially given the rapid accumulation of gene sequences. Supplementation with efficient computational methods is urgently needed.
RESULTS: In this study, we developed a new tool, 4mCPred, for predicting 4mC sites in Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, Escherichia coli, Geoalkalibacter subterraneus and Geobacter pickeringii. 4mCPred consists of two independent models, 4mCPred_I and 4mCPred_II, for each species. The predictive results of independent and cross-species tests demonstrated that the performance of 4mCPred_I is a useful tool. To identify position-specific trinucleotide propensity (PSTNP) and electron-ion interaction potential features, we used the F-score method to construct predictive models and to compare their PSTNP features. Compared with other existing predictors, 4mCPred achieved much higher accuracies in rigorous jackknife and independent tests. We also analyzed the importance of different features in detail.
AVAILABILITY AND IMPLEMENTATION: The web-server 4mCPred is accessible at http://server.malab.cn/4mCPred/index.jsp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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Year:  2019        PMID: 30052767     DOI: 10.1093/bioinformatics/bty668

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  35 in total

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2.  Deep4mC: systematic assessment and computational prediction for DNA N4-methylcytosine sites by deep learning.

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Journal:  Brief Bioinform       Date:  2021-05-20       Impact factor: 11.622

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Journal:  Nucleic Acids Res       Date:  2021-06-04       Impact factor: 16.971

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6.  iDNA-MT: Identification DNA Modification Sites in Multiple Species by Using Multi-Task Learning Based a Neural Network Tool.

Authors:  Xiao Yang; Xiucai Ye; Xuehong Li; Lesong Wei
Journal:  Front Genet       Date:  2021-03-31       Impact factor: 4.599

7.  mRNALocater: Enhance the prediction accuracy of eukaryotic mRNA subcellular localization by using model fusion strategy.

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9.  4mCPred-MTL: Accurate Identification of DNA 4mC Sites in Multiple Species Using Multi-Task Deep Learning Based on Multi-Head Attention Mechanism.

Authors:  Rao Zeng; Song Cheng; Minghong Liao
Journal:  Front Cell Dev Biol       Date:  2021-05-10

10.  i4mC-EL: Identifying DNA N4-Methylcytosine Sites in the Mouse Genome Using Ensemble Learning.

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Journal:  Biomed Res Int       Date:  2021-05-29       Impact factor: 3.411

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