ArticleFrontiers in genetics2019
An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- PLysPTM-HGNN: predicting lysine PTM sites of proteins using hybrid graph neural networks.BMC bioinformatics · 2026Article
- Crossing epigenetic frontiers: the intersection of novel histone modifications and diseases.Signal transduction and targeted therapy · 2024Review
- The mechanisms, regulations, and functions of histone lysine crotonylation.Cell death discovery · 2024Review
- Functions and mechanisms of protein lysine butyrylation (Kbu): Therapeutic implications in human diseases.Genes & diseases · 2023Review
- MultiScale-CNN-4mCPred: a multi-scale CNN and adaptive embedding-based method for mouse genome DNA N4-methylcytosine prediction.BMC bioinformatics · 2023Article
- Post-Translational Modifications by Lipid Metabolites during the DNA Damage Response and Their Role in Cancer.Biomolecules · 2022Review
- Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition.Biomolecules · 2022Article
- PRIP: A Protein-RNA Interface Predictor Based on Semantics of Sequences.Life (Basel, Switzerland) · 2022Article
- A Transfer Learning-Based Approach for Lysine Propionylation Prediction.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Butyrylation plays a crucial role in the cellular processes. Due to limit of techniques, it is a challenging task to identify histone butyrylation sites on a large scale. To fill the gap, we propose an approach based on information entropy and machine learning for computationally identifying histone butyrylation sites. The proposed method achieves 0.92 of area under the receiver operating characteristic (ROC) curve over the training set by 3-fold cross validation and 0.80 over the testing set by independent test. Feature analysis implies that amino acid residues in the down/upstream of butyrylation sites would exhibit specific sequence motif to a certain extent. Functional analysis suggests that histone butyrylation was most possibly associated with four pathways (systemic lupus erythematosus, alcoholism, viral carcinogenesis and transcriptional misregulation in cancer), was involved in binding with other molecules, processes of biosynthesis, assembly, arrangement or disassembly and was located in such complex as consists of DNA, RNA, protein,
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