ArticleGenes2020
Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features.
Article in Genes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 28 citations in OpenAlex.
- Deep learning framework with interpretable feature selection for accurate SUMOylation site prediction.Scientific reports · 2026Article
- Processes and therapeutic perspectives of acylation modifications of lysine and cysteine in tumors.Cell communication and signaling : CCS · 2026Review
- BiGKbhb: a bi-directional gated recurrent unit model for predicting lysine β-hydroxybutyrylation sites.BMC genomics · 2026Article
- Substrate and Functional Diversity of Protein Lysine Post-translational Modifications.Genomics, proteomics & bioinformatics · 2024Review
- GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier.BMC genomics · 2023Article
- Article
- MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.Frontiers in medicine · 2022Article
- DeepFeature: feature selection in nonimage data using convolutional neural network.Briefings in bioinformatics · 2021Article
- A chemical field guide to histone nonenzymatic modifications.Current opinion in chemical biology · 2021Review
- Functions and Mechanisms of Lysine Glutarylation in Eukaryotes.Frontiers in cell and developmental biology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Post Translational Modification (PTM) is defined as the alteration of protein sequence upon interaction with different macromolecules after the translation process. Glutarylation is considered one of the most important PTMs, which is associated with a wide range of cellular functioning, including metabolism, translation, and specified separate subcellular localizations. During the past few years, a wide range of computational approaches has been proposed to predict Glutarylation sites. However, despite all the efforts that have been made so far, the prediction performance of the Glutarylation sites has remained limited. One of the main challenges to tackle this problem is to extract features with significant discriminatory information. To address this issue, we propose a new machine learning method called BiPepGlut using the concept of a bi-peptide-based evolutionary method for feature extraction. To build this model, we also use the Extra-Trees (ET) classifier for the classification purpose, which, to the best of our knowledge, has never been used for this task. Our results demonstrate BiPepGlut is able to significantly outperform previously proposed models to tackle this problem. BiPepGlut achieves 92.0%, 84.8%, 95.6%, 0.82, and 0.88 in accuracy, sensitivity, specificity, Matthew's Correlation Coefficient, and F1-score, respectively. BiPepGlut is implemented as a publicly available online predictor.
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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.