ArticleACS omega2020
Computational Prediction of Protein Arginine Methylation Based on Composition-Transition-Distribution Features.
Article in ACS omega, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- DeepBBMC biology · 2025Article
- Sitetack: a deep learning model that improves PTM prediction by using known PTMs.Bioinformatics (Oxford, England) · 2024Article
- MultiScale-CNN-4mCPred: a multi-scale CNN and adaptive embedding-based method for mouse genome DNA N4-methylcytosine prediction.BMC bioinformatics · 2023Article
- FEPS: A Tool for Feature Extraction from Protein Sequence.Methods in molecular biology (Clifton, N.J.) · 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
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Arginine methylation is one of the most essential protein post-translational modifications. Identifying the site of arginine methylation is a critical problem in biology research. Unfortunately, biological experiments such as mass spectrometry are expensive and time-consuming. Hence, predicting arginine methylation by machine learning is an alternative fast and efficient way. In this paper, we focus on the systematic characterization of arginine methylation with composition-transition-distribution (CTD) features. The presented framework consists of three stages. In the first stage, we extract CTD features from 1750 samples and exploit decision tree to generate accurate prediction. The accuracy of prediction can reach 96%. In the second stage, the support vector machine can predict the number of arginine methylation sites with 0.36
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Registered trials
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