ArticleFrontiers in cell and developmental biology2020
iPromoter-5mC: A Novel Fusion Decision Predictor for the Identification of 5-Methylcytosine Sites in Genome-Wide DNA Promoters.
Article in Frontiers in cell and developmental biology, 2020. 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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20 citing papers in PubMed, 43 citations in OpenAlex.
- DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.PLoS computational biology · 2026Article
- TCN-5mC: a predictor of 5-methylcytosine sites based on multi-feature fusion and TCN-inspired block networks.Frontiers in genetics · 2026Article
- DNA Methylation Recognition Using Hybrid Deep Learning with Dual Nucleotide Visualization Fusion Feature Encoding.Interdisciplinary sciences, computational life sciences · 2025Article
- Deep5mC: Predicting 5-methylcytosine (5mC) methylation status using a deep learning transformer approach.Computational and structural biotechnology journal · 2025Article
- Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites.Biology · 2024Review
- Associations between placental hydroxymethylation and birthweight.Epigenetics reports · 2024Article
- StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning.BMC genomics · 2023Article
- BERT-5mC: an interpretable model for predicting 5-methylcytosine sites of DNA based on BERT.PeerJ · 2023Article
- Article
- Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning.International journal of molecular sciences · 2022Review
- Regulators of epigenetic change in ferroptosis‑associated cancer (Review).Oncology reports · 2022Review
- iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations.Genome biology · 2022Article
- IBPred: A sequence-based predictor for identifying ion binding protein in phage.Computational and structural biotechnology journal · 2022Article
- A method for identifying moonlighting proteins based on linear discriminant analysis and bagging-SVM.Frontiers in genetics · 2022Article
- The Characterization of Structure and Prediction for Aquaporin in Tumour Progression by Machine Learning.Frontiers in cell and developmental biology · 2022Article
- BiLSTM-5mC: A Bidirectional Long Short-Term Memory-Based Approach for Predicting 5-Methylcytosine Sites in Genome-Wide DNA Promoters.Molecules (Basel, Switzerland) · 2021Article
- DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins.Frontiers in pharmacology · 2021Review
- Bioinformatics Research on Drug Sensitivity Prediction.Frontiers in pharmacology · 2021Review
- A SNARE Protein Identification Method Based on iLearnPlus to Efficiently Solve the Data Imbalance Problem.Frontiers in genetics · 2021Article
- Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D.Frontiers in genetics · 2021Article
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Authors and funding
3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The hypomethylation of the whole cancer genome and the hypermethylation of the promoter of specific tumor suppressor genes are the important reasons for the rapid proliferation of cancer cells. Therefore, obtaining the distribution of 5-methylcytosine (5mC) in promoters is a key step to further understand the relationship between promoter methylation and mRNA gene expression regulation. Large-scale detection of DNA 5mC through wet experiments is still time-consuming and laborious. Therefore, it is urgent to design a method for identifying the 5mC site of genome-wide DNA promoters. Based on promoter methylation data of the small cell lung cancer (SCLC) from the database named cancer cell line Encyclopedia (CCLE), we built a fusion decision predictor called iPromoter-5mC for identifying methylation modification sites in promoters using deep neural network (DNN). One-Hot Encoding (One-hot) was used to encode the promoter samples for the classification. The method achieves average AUC of 0.957 on the independent testing dataset, indicating that our predictor is robust and reliable. A user-friendly web-server called iPromoter-5mC could be freely accessible at http://www.jci-bioinfo.cn/iPromoter-5mC, which will provide simple and effective means for users to study promoter 5mC modification. The source code of the proposed methods is freely available for academic research at https://github.com/zlwuxi/iPromoter-5mC.
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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.