ArticleBioinformatics (Oxford, England)2017
MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 papers.
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125 citing papers in PubMed.
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- KLSD: A Curated Kinase-Ligand Database Mapping Selectivity Landscapes and Polypharmacology.ACS omega · 2026Article
- Dormancy Versus Germination: 3D Protein Modeling and Evolutionary Analyses Define the Roles of Genetic Variants in the Barley MKK3 Enzyme.International journal of molecular sciences · 2026Article
- A bioinformatics pipeline for identifying clinically relevant immune and adhesion biomarkers in thyroid risk stratification.Frontiers in molecular biosciences · 2026Article
- Prioritizing missense mutations via a deep learning phosphorylation prediction model.Human genomics · 2025Article
- A deep learning framework for lysine 2-hydroxyisobutyrylation site prediction using evolutionary feature representation.Scientific reports · 2025Article
- E2 Tyrosine 102 Regulates MmuPV1 Pathogenesis In Vivo.Pathogens (Basel, Switzerland) · 2025Article
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- An efficient machine-learning framework for predicting protein post-translational modification sites.Scientific reports · 2025Article
- Ser/Thr phosphorylation ofmBio · 2025Article
- MTPrompt-PTM: A Multi-Task Method for Post-Translational Modification Prediction Using Prompt Tuning on a Structure-Aware Protein Language Model.Biomolecules · 2025Article
- Cloning and Spatiotemporal Expression Analysis ofLife (Basel, Switzerland) · 2025Article
- DCHS1 Modulates Forebrain Proportions in Modern Humans via a Glycosylation Change.bioRxiv : the preprint server for biology · 2025Article
- PhosF3C: a feature fusion architecture with fine-tuned protein language model and conformer for prediction of general phosphorylation site.Briefings in bioinformatics · 2025Article
- Inferring kinase-phosphosite regulation from phosphoproteome-enriched cancer multi-omics datasets.Briefings in bioinformatics · 2025Article
65 more citing papers are in PubMed but not listed here.
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7 authors.
Funding
Abstract
motivationComputational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of phosphorylation site prediction.
resultsWe present MusiteDeep, the first deep-learning framework for predicting general and kinase-specific phosphorylation sites. MusiteDeep takes raw sequence data as input and uses convolutional neural networks with a novel two-dimensional attention mechanism. It achieves over a 50% relative improvement in the area under the precision-recall curve in general phosphorylation site prediction and obtains competitive results in kinase-specific prediction compared to other well-known tools on the benchmark data. AVAILABILITY AND IMPLEMENTATION: MusiteDeep is provided as an open-source tool available at https://github.com/duolinwang/MusiteDeep. CONTACT: xudong@missouri.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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