ReviewGenomics, proteomics & bioinformatics2023
A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction.
Review in Genomics, proteomics & bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- GenPTM: A Generalizable Framework for Protein Post-Translational Modification Information Extraction from the Scientific Literature.Research square · 2026Article
- UniPTMs: a unified multi-type PTM site prediction model via master-slave architecture-based multi-stage fusion strategy and hierarchical contrastive loss.BMC bioinformatics · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Subtimizer: Computational Workflow for Structure-Guided Design of Potent and Selective Kinase Peptide Substrates.Journal of chemical information and modeling · 2026Article
- Protein Post-Translational Modifications in the Regulation of Ferroptosis: New Opportunities and Challenges for Cancer Immunotherapy.International journal of biological sciences · 2026Review
- Uncovering Enzyme-Specific Post-Translational Modifications: An Overview of Current Methods.Proteomes · 2025Review
- Recent Advances, Challenges, and Functional Applications of Protein Chemical Modification in the Food Industry.Foods (Basel, Switzerland) · 2025Review
- Integrating Redox Proteomics and Computational Modeling to Decipher Thiol-Based Oxidative Post-Translational Modifications (oxiPTMs) in Plant Stress Physiology.International journal of molecular sciences · 2025Review
- MTPrompt-PTM: A Multi-Task Method for Post-Translational Modification Prediction Using Prompt Tuning on a Structure-Aware Protein Language Model.Biomolecules · 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
- MMFuncPhos: A Multi-Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Kinase-substrate prediction using an autoregressive model.Computational and structural biotechnology journal · 2025Article
- Protein modification in neurodegenerative diseases.MedComm · 2024Review
- Article
- Interaction of Soybean (Plants (Basel, Switzerland) · 2024Article
- Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences.Database : the journal of biological databases and curation · 2024Review
- Machine learning-based approaches for ubiquitination site prediction in human proteins.BMC bioinformatics · 2023Article
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 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes. Disorders in the phosphorylation process lead to multiple diseases, including neurological disorders and cancers. The purpose of this review is to organize this body of knowledge associated with phosphorylation site (p-site) prediction to facilitate future research in this field. At first, we comprehensively review all related databases and introduce all steps regarding dataset creation, data preprocessing, and method evaluation in p-site prediction. Next, we investigate p-site prediction methods, which are divided into two computational groups: algorithmic and machine learning (ML). Additionally, it is shown that there are basically two main approaches for p-site prediction by ML: conventional and end-to-end deep learning methods, both of which are given an overview. Moreover, this review introduces the most important feature extraction techniques, which have mostly been used in p-site prediction. Finally, we create three test sets from new proteins related to the released version of the database of protein post-translational modifications (dbPTM) in 2022 based on general and human species. Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release, distinct from those in the dbPTM 2019 release, reveals their limitations. In other words, the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research papers.
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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 Socratic graph.