ArticleBMC bioinformatics2019
DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 86 citations in OpenAlex.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Deep learning-driven decoding of ubiquitination: from regulatory mechanisms to targeted protein degradation.Biology direct · 2026Review
- Methods to Study the Molecular Mechanism and Drive the Design of Protein Degraders.Chemical reviews · 2026Review
- Post-translational Modifications in Proteins: Prediction Methods, Biological Functions, and Diseases.MedComm · 2026Review
- Target-stabilized base editors enable robust high-fidelity RNA editing.Nature communications · 2026Article
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- BiGKbhb: a bi-directional gated recurrent unit model for predicting lysine β-hydroxybutyrylation sites.BMC genomics · 2026Article
- Opening the black box: insights into ubiquitin-mediated control of innate antiviral immunity and AI-enhanced therapeutics.Frontiers in immunology · 2026Review
- Deep learning for regulatory genomics: a survey of models, challenges, and applications.Bioinformatics advances · 2026Review
- A deep learning framework for lysine 2-hydroxyisobutyrylation site prediction using evolutionary feature representation.Scientific reports · 2025Article
- Ubigo-X: Protein ubiquitination site prediction using ensemble learning with image-based feature representation and weighted voting.Computational and structural biotechnology journal · 2025Article
- iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.Current medicinal chemistry · 2025Article
- Insight into Protein Engineering: FromCurrent pharmaceutical design · 2025Review
- Multimodal deep learning for predicting protein ubiquitination sites.Bioinformatics advances · 2025Article
- Predictive modeling for ubiquitin proteins through advanced machine learning technique.Heliyon · 2024Article
- Machine learning-based approaches for ubiquitination site prediction in human proteins.BMC bioinformatics · 2023Article
- Applications of deep learning in understanding gene regulation.Cell reports methods · 2023Review
- Thirty years of molecular dynamics simulations on posttranslational modifications of proteins.Physical chemistry chemical physics : PCCP · 2022Review
- Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.International journal of molecular sciences · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundProtein ubiquitination occurs when the ubiquitin protein binds to a target protein residue of lysine (K), and it is an important regulator of many cellular functions, such as signal transduction, cell division, and immune reactions, in eukaryotes. Experimental and clinical studies have shown that ubiquitination plays a key role in several human diseases, and recent advances in proteomic technology have spurred interest in identifying ubiquitination sites. However, most current computing tools for predicting target sites are based on small-scale data and shallow machine learning algorithms.
resultsAs more experimentally validated ubiquitination sites emerge, we need to design a predictor that can identify lysine ubiquitination sites in large-scale proteome data. In this work, we propose a deep learning predictor, DeepUbi, based on convolutional neural networks. Four different features are adopted from the sequences and physicochemical properties. In a 10-fold cross validation, DeepUbi obtains an AUC (area under the Receiver Operating Characteristic curve) of 0.9, and the accuracy, sensitivity and specificity exceeded 85%. The more comprehensive indicator, MCC, reaches 0.78. We also develop a software package that can be freely downloaded from https://github.com/Sunmile/DeepUbi .
conclusionOur results show that DeepUbi has excellent performance in predicting ubiquitination based on large data.
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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.