ArticleScientific reports2015
Position-specific prediction of methylation sites from sequence conservation based on information theory.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 20 citations in OpenAlex.
- Leveraging learned representations and multitask learning for lysine methylation site discovery.Scientific reports · 2026Article
- Leveraging learned representations and multitask learning for lysine methylation site discovery.bioRxiv : the preprint server for biology · 2025Article
- Protein-Specific Prediction of RNA-Binding Sites Based on Information Entropy.Computational intelligence and neuroscience · 2022Article
- Locus-specific DNA methylation prediction in cord blood and placenta.Epigenetics · 2019Article
- An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation.Frontiers in genetics · 2019Article
- Effective prediction of bacterial type IV secreted effectors by combined features of both C-termini and N-termini.Journal of computer-aided molecular design · 2017Article
- Fangorn Forest (F2): a machine learning approach to classify genes and genera in the family Geminiviridae.BMC bioinformatics · 2017Article
- Using oriented peptide array libraries to evaluate methylarginine-specific antibodies and arginine methyltransferase substrate motifs.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein methylation plays vital roles in many biological processes and has been implicated in various human diseases. To fully understand the mechanisms underlying methylation for use in drug design and work in methylation-related diseases, an initial but crucial step is to identify methylation sites. The use of high-throughput bioinformatics methods has become imperative to predict methylation sites. In this study, we developed a novel method that is based only on sequence conservation to predict protein methylation sites. Conservation difference profiles between methylated and non-methylated peptides were constructed by the information entropy (IE) in a wider neighbor interval around the methylation sites that fully incorporated all of the environmental information. Then, the distinctive neighbor residues were identified by the importance scores of information gain (IG). The most representative model was constructed by support vector machine (SVM) for Arginine and Lysine methylation, respectively. This model yielded a promising result on both the benchmark dataset and independent test set. The model was used to screen the entire human proteome, and many unknown substrates were identified. These results indicate that our method can serve as a useful supplement to elucidate the mechanism of protein methylation and facilitate hypothesis-driven experimental design and validation.
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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.