Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species
Abstract
Motivation: As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) is recently shown to play crucial roles in restriction–modification systems. For better understanding of their functional mechanisms, it is fundamentally important to identify 4mC modification. Machine learning methods have recently emerged as an effective and efficient approach for the high-throughput identification of 4mC sites, although high predictive error rates are still challenging for existing methods. Therefore, it is highly desirable to develop a computational method to more accurately identify m4C sites. Results: In this study, we propose a machine learning based predictor, namely 4mcPred-SVM, for the genome-wide detection of DNA 4mC sites. In this predictor, we present a new feature representation algorithm that sufficiently exploits sequence-based information. To improve the feature representation ability, we use a two-step feature optimization strategy, thereby obtaining the most representative features. Using the resulting features and Support Vector Machine (SVM), we adaptively train the optimal models for different species. Comparative results on benchmark datasets from six species indicate that our predictor is able to achieve generally better performance in predicting 4mC sites as compared to the state-of-the-art predictors. Importantly, the sequence-based features can reliably and robust predict 4mC sites, facilitating the discovery of potentially important sequence characteristics for the prediction of 4mC sites. Availability and implementation: The user-friendly webserver that implements the proposed 4mcPred-SVM is well established, and is freely accessible at http://server.malab.cn/4mcPred-SVM. Supplementary information: Supplementary data are available at Bioinformatics online.
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Most existing works use the ADNI database, which is based on MRI.
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From An Efficient Classifier for Alzheimer’s Disease Genes Identification (2018) · cites this paper as [40]
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- Authors
- Wei, L., Luan, S., Nagai, L. A. E., Su, R., & Zou, Q.
- Affiliations
- Tianjin University (CN)
- Lab of Functional Analysis In Silico, Institute of Medical Science, University of Tokyo, Tokyo, Japan
- University of Electronic Science and Technology of China (CN)
- Corresponding author
- Su, Ran
- Zou, Quan
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-09-19
- Volume
- 35
- Issue
- 8
- Pages
- 1326-1333
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2891420113
- Cited by
- 175
- References
- 50
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 15 · 2023: 18 · 2022: 41 · 2021: 35 · 2020: 28 · 2019: 28 · 2018: 5
- FWCI
- 8.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion, public-domain)
- PubMed (publishedVersion)
- Funders
- National Key R&D Program of China (SQ2018YFC090002)
- National Natural Science Foundation of China (61701340, 61702361, 61771331)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- DNA methylation, restriction-modification systems, epigenetic modifications, sequence-based features, feature optimization, web server
- MeSH terms
- Machine Learning, Algorithms, DNA, Genome, Support Vector Machine
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