ArticleMolecular therapy. Nucleic acids2019
Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation.
Article in Molecular therapy. Nucleic acids, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.
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93 citing papers in PubMed.
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- H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA.Briefings in bioinformatics · 2023Article
- A First Computational Frame for Recognizing Heparin-Binding Protein.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- MultiScale-CNN-4mCPred: a multi-scale CNN and adaptive embedding-based method for mouse genome DNA N4-methylcytosine prediction.BMC bioinformatics · 2023Article
- DRSN4mCPred: accurately predicting sites of DNA N4-methylcytosine using deep residual shrinkage network for diagnosis and treatment of gastrointestinal cancer in the precision medicine era.Frontiers in medicine · 2023Article
- Accurately identifying hemagglutinin using sequence information and machine learning methods.Frontiers in medicine · 2023Article
- Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition.Frontiers in genetics · 2023Article
- 4acCPred: Weakly supervised prediction ofMolecular therapy. Nucleic acids · 2022Article
33 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
DNA N4-methylcytosine (4mC) is an important genetic modification and plays crucial roles in differentiation between self and non-self DNA and in controlling DNA replication, cell cycle, and gene-expression levels. Accurate 4mC site identification is fundamental to improve the understanding of 4mC biological functions and mechanisms. Hence, it is necessary to develop in silico approaches for efficient and high-throughput 4mC site identification. Although some bioinformatic tools have been developed in this regard, their prediction accuracy and generalizability require improvement to optimize their usability in practical applications. For this purpose, we here proposed Meta-4mCpred, a meta-predictor for 4mC site prediction. In Meta-4mCpred, we employed a feature representation learning scheme and generated 56 probabilistic features based on four different machine-learning algorithms and seven feature encodings covering diverse sequence information, including compositional, physicochemical, and position-specific information. Subsequently, the probabilistic features were used as an input to support vector machine and developed a final meta-predictor. To the best of our knowledge, this is the first meta-predictor for 4mC site prediction. Cross-validation results show that Meta-4mCpred achieved an overall average accuracy of 84.2% from six different species, which is ∼2%-4% higher than those attainable using the state-of-the-art predictors. Furthermore, Meta-4mCpred achieved an overall average accuracy of 86% on independent datasets evaluation, which is over 4% higher than those yielded by the state-of-the-art predictors. The user-friendly webserver employed to implement the proposed Meta-4mCpred is freely accessible at http://thegleelab.org/Meta-4mCpred.
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