ArticleMolecular omics2020
DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins.
Article in Molecular omics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 37 citations in OpenAlex.
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Post-translational Modifications in Proteins: Prediction Methods, Biological Functions, and Diseases.MedComm · 2026Review
- Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model.Nature communications · 2024Article
- Prediction of leukemia peptides using convolutional neural network and protein compositions.BMC cancer · 2024Article
- iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features.BMC genomics · 2022Article
- CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence.Frontiers in genetics · 2022Article
- Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites.Scientific reports · 2021Article
- SSMFN: a fused spatial and sequential deep learning model for methylation site prediction.PeerJ. Computer science · 2021Article
- DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites.Frontiers in cell and developmental biology · 2021Article
- Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features.Genes · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Methylation, which is one of the most prominent post-translational modifications on proteins, regulates many important cellular functions. Though several model-based methylation site predictors have been reported, all existing methods employ machine learning strategies, such as support vector machines and random forest, to predict sites of methylation based on a set of "hand-selected" features. As a consequence, the subsequent models may be biased toward one set of features. Moreover, due to the large number of features, model development can often be computationally expensive. In this paper, we propose an alternative approach based on deep learning to predict arginine methylation sites. Our model, which we termed DeepRMethylSite, is computationally less expensive than traditional feature-based methods while eliminating potential biases that can arise through features selection. Based on independent testing on our dataset, DeepRMethylSite achieved efficiency scores of 68%, 82% and 0.51 with respect to sensitivity (SN), specificity (SP) and Matthew's correlation coefficient (MCC), respectively. Importantly, in side-by-side comparisons with other state-of-the-art methylation site predictors, our method performs on par or better in all scoring metrics tested.
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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.