ArticleBioinformatics (Oxford, England)2020
DeepKinZero: zero-shot learning for predicting kinase-phosphosite associations involving understudied kinases.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 52 citations in OpenAlex.
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- Article
- DARKIN: a zero-shot benchmark for phosphosite-dark kinase association using protein language models.Bioinformatics (Oxford, England) · 2025Article
- KSMoFinder - Knowledge graph embedding of proteins and motifs for predicting kinases of human phosphosites.bioRxiv : the preprint server for biology · 2025Article
- Bias-aware training and evaluation of link prediction algorithms in network biology.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Inferring kinase-phosphosite regulation from phosphoproteome-enriched cancer multi-omics datasets.Briefings in bioinformatics · 2025Article
- Kinase-substrate prediction using an autoregressive model.Computational and structural biotechnology journal · 2025Article
- KSMoFinder-knowledge graph embedding of proteins and motifs for predicting kinases of human phosphosites.Bioinformatics advances · 2025Article
- Experimental measurement and computational prediction of bacterial Hanks-type Ser/Thr signaling system regulatory targets.Molecular microbiology · 2024Review
- Bringing CAM photosynthesis to the table: Paving the way for resilient and productive agricultural systems in a changing climate.Plant communications · 2024Review
- Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery.Proteomes · 2023Review
- KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites.Genomics, proteomics & bioinformatics · 2023Article
- Prediction of Kinase-Substrate Associations Using The Functional Landscape of Kinases and Phosphorylation Sites.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
- Off-targetP ML: an open source machine learning framework for off-target panel safety assessment of small molecules.Journal of cheminformatics · 2022Article
- Mini-review: Recent advances in post-translational modification site prediction based on deep learning.Computational and structural biotechnology journal · 2022Review
- Labels in a haystack: Approaches beyond supervised learning in biomedical applications.Patterns (New York, N.Y.) · 2021Review
- Managing of Unassigned Mass Spectrometric Data by Neural Network for Cancer Phenotypes Classification.Journal of personalized medicine · 2021Article
- Pretraining model for biological sequence data.Briefings in functional genomics · 2021Review
- Robust inference of kinase activity using functional networks.Nature communications · 2021Article
- Representation learning applications in biological sequence analysis.Computational and structural biotechnology journal · 2021Review
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
motivationProtein phosphorylation is a key regulator of protein function in signal transduction pathways. Kinases are the enzymes that catalyze the phosphorylation of other proteins in a target-specific manner. The dysregulation of phosphorylation is associated with many diseases including cancer. Although the advances in phosphoproteomics enable the identification of phosphosites at the proteome level, most of the phosphoproteome is still in the dark: more than 95% of the reported human phosphosites have no known kinases. Determining which kinase is responsible for phosphorylating a site remains an experimental challenge. Existing computational methods require several examples of known targets of a kinase to make accurate kinase-specific predictions, yet for a large body of kinases, only a few or no target sites are reported.
resultsWe present DeepKinZero, the first zero-shot learning approach to predict the kinase acting on a phosphosite for kinases with no known phosphosite information. DeepKinZero transfers knowledge from kinases with many known target phosphosites to those kinases with no known sites through a zero-shot learning model. The kinase-specific positional amino acid preferences are learned using a bidirectional recurrent neural network. We show that DeepKinZero achieves significant improvement in accuracy for kinases with no known phosphosites in comparison to the baseline model and other methods available. By expanding our knowledge on understudied kinases, DeepKinZero can help to chart the phosphoproteome atlas. AVAILABILITY AND IMPLEMENTATION: The source codes are available at https://github.com/Tastanlab/DeepKinZero. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.