ArticleBioinformatics (Oxford, England)2019
DeepPhos: prediction of protein phosphorylation sites with deep learning.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 2 of them syntheses that pooled it.
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Who cites it
88 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry.Nature communications · 2023Pooled it
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- Predicting Functional Diversity Within Enzyme Families: A Practical Perspective.Chembiochem : a European journal of chemical biology · 2026Review
- Toward Rational Design of Precision-Fermented Milk Proteins: Integrating Cross-Species Selection, Post-Translational Modification, and AI Optimization.Comprehensive reviews in food science and food safety · 2026Review
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Learning the PTM code through a coarse-to-fine mechanism-aware framework.Nature communications · 2026Article
- CMV-GLA: contrastive multi-view graph layer attention for predicting phosphorylation site-disease associations.BMC bioinformatics · 2026Article
- Post-translational Modifications in Proteins: Prediction Methods, Biological Functions, and Diseases.MedComm · 2026Review
- Clinical Applications of Phosphoproteomics: Illuminating Cancer Signaling and Enabling Rational Therapeutic Strategies.Cancer science · 2026Review
- Low-input deep learning platform for citrullinated peptide identification, autoantigen discovery and rheumatoid arthritis treatment stratification.Nature biomedical engineering · 2026Article
- ProtPSP: Determination of Protein Phosphorylation Sites with the Protein Large Language Model.ACS omega · 2025Article
- Prioritizing missense mutations via a deep learning phosphorylation prediction model.Human genomics · 2025Article
- Post-Translational Modifications in Respiratory Virus Infection: Recent Insights into the Development of In Vitro Models.International journal of molecular sciences · 2025Review
- A deep learning framework for lysine 2-hydroxyisobutyrylation site prediction using evolutionary feature representation.Scientific reports · 2025Article
- [Prediction of protein Kbhb sites based on learnable feature embedding].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- DeepMVP: deep learning models trained on high-quality data accurately predict PTM sites and variant-induced alterations.Nature methods · 2025Article
- An efficient machine-learning framework for predicting protein post-translational modification sites.Scientific reports · 2025Article
- The structural view of the protein PGD-219aa encoded by the circular RNA CircPGD.Journal of molecular modeling · 2025Article
- Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment.BMC genomics · 2025Article
- MlyPredCSED: based on extreme point deviation compensated clustering combined with cross-scale convolutional neural networks to predict multiple lysine sites in human.Briefings in bioinformatics · 2025Article
28 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationPhosphorylation is the most studied post-translational modification, which is crucial for multiple biological processes. Recently, many efforts have been taken to develop computational predictors for phosphorylation site prediction, but most of them are based on feature selection and discriminative classification. Thus, it is useful to develop a novel and highly accurate predictor that can unveil intricate patterns automatically for protein phosphorylation sites.
resultsIn this study we present DeepPhos, a novel deep learning architecture for prediction of protein phosphorylation. Unlike multi-layer convolutional neural networks, DeepPhos consists of densely connected convolutional neuron network blocks which can capture multiple representations of sequences to make final phosphorylation prediction by intra block concatenation layers and inter block concatenation layers. DeepPhos can also be used for kinase-specific prediction varying from group, family, subfamily and individual kinase level. The experimental results demonstrated that DeepPhos outperforms competitive predictors in general and kinase-specific phosphorylation site prediction. AVAILABILITY AND IMPLEMENTATION: The source code of DeepPhos is publicly deposited at https://github.com/USTCHIlab/DeepPhos. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.