ArticleInternational journal of molecular sciences2018
A Novel Computational Method for Detecting DNA Methylation Sites with DNA Sequence Information and Physicochemical Properties.
Article in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 44 citations in OpenAlex.
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- Single-Cell Transcriptomic Approaches for Decoding Non-Coding RNA Mechanisms in Colorectal Cancer.Non-coding RNA · 2025Review
- Methods in DNA methylation array dataset analysis: A review.Computational and structural biotechnology journal · 2024Review
- Advancing epigenetic profiling in cervical cancer: machine learning techniques for classifying DNA methylation patterns.3 Biotech · 2024Article
- iCpG-Pos: an accurate computational approach for identification of CpG sites using positional features on single-cell whole genome sequence data.Bioinformatics (Oxford, England) · 2023Article
- Using the Random Forest for Identifying Key Physicochemical Properties of Amino Acids to Discriminate Anticancer and Non-Anticancer Peptides.International journal of molecular sciences · 2023Article
- DNA Methylation and Non-Coding RNAs during Tissue-Injury Associated Pain.International journal of molecular sciences · 2022Review
- Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics.Membranes · 2021Review
- Article
- ncPro-ML: An integrated computational tool for identifying non-coding RNA promoters in multiple species.Computational and structural biotechnology journal · 2020Article
- Its2vec: Fungal Species Identification Using Sequence Embedding and Random Forest Classification.BioMed research international · 2020Article
- Identification of Genome Sequences of Polyphosphate-Accumulating Organisms by Machine Learning.Frontiers in cell and developmental biology · 2020Article
- iPseU-CNN: Identifying RNA Pseudouridine Sites Using Convolutional Neural Networks.Molecular therapy. Nucleic acids · 2019Article
- LightCpG: a multi-view CpG sites detection on single-cell whole genome sequence data.BMC genomics · 2019Article
- A Positive Causal Influence of IL-18 Levels on the Risk of T2DM: A Mendelian Randomization Study.Frontiers in genetics · 2019Article
- Diagnosis of Brain Diseases via Multi-Scale Time-Series Model.Frontiers in neuroscience · 2019Article
- Discovering Cancer Subtypes via an Accurate Fusion Strategy on Multiple Profile Data.Frontiers in genetics · 2019Article
- Predicting Ion Channels Genes and Their Types With Machine Learning Techniques.Frontiers in genetics · 2019Article
- Application of Machine Learning in Microbiology.Frontiers in microbiology · 2019Review
- Identifying Plant Pentatricopeptide Repeat Coding Gene/Protein Using Mixed Feature Extraction Methods.Frontiers in plant science · 2018Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA methylation is an important biochemical process, and it has a close connection with many types of cancer. Research about DNA methylation can help us to understand the regulation mechanism and epigenetic reprogramming. Therefore, it becomes very important to recognize the methylation sites in the DNA sequence. In the past several decades, many computational methods-especially machine learning methods-have been developed since the high-throughout sequencing technology became widely used in research and industry. In order to accurately identify whether or not a nucleotide residue is methylated under the specific DNA sequence context, we propose a novel method that overcomes the shortcomings of previous methods for predicting methylation sites. We use
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.