ArticleCurrent genomics2020
Computational Identification of Lysine Glutarylation Sites Using Positive-Unlabeled Learning.
Article in Current genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 14 citations in OpenAlex.
- Identification of potential riboswitch elements in Homo sapiens mRNA 5'UTR sequences using positive-unlabeled machine learning.PloS one · 2025Article
- Positive-unlabeled learning to infer protection status and identify correlates in vaccine efficacy field trials.iScience · 2024Article
- GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier.BMC genomics · 2023Article
- Insights into the post-translational modification and its emerging role in shaping the tumor microenvironment.Signal transduction and targeted therapy · 2021Review
- Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance.Scientific reports · 2021Article
- Functions and Mechanisms of Lysine Glutarylation in Eukaryotes.Frontiers in cell and developmental biology · 2021Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAs a new type of protein acylation modification, lysine glutarylation has been found to play a crucial role in metabolic processes and mitochondrial functions. To further explore the biological mechanisms and functions of glutarylation, it is significant to predict the potential glutarylation sites. In the existing glutarylation site predictors, experimentally verified glutarylation sites are treated as positive samples and non-verified lysine sites as the negative samples to train predictors. However, the non-verified lysine sites may contain some glutarylation sites which have not been experimentally identified yet.
methodsIn this study, experimentally verified glutarylation sites are treated as the positive samples, whereas the remaining non-verified lysine sites are treated as unlabeled samples. A bioinformatics tool named PUL-GLU was developed to identify glutarylation sites using a positive-unlabeled learning algorithm.
resultsExperimental results show that PUL-GLU significantly outperforms the current glutarylation site predictors. Therefore, PUL-GLU can be a powerful tool for accurate identification of protein glutarylation sites.
conclusionA user-friendly web-server for PUL-GLU is available at http://bioinform.cn/pul_glu/.
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