ReviewComputational and structural biotechnology journal2022
Mini-review: Recent advances in post-translational modification site prediction based on deep learning.
Review in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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.
The trial behind it
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Who cites it
29 citing papers in PubMed.
- 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
- SeqHIVE: a Python package to convert the biological sequences to informative vectors for sequence property predictions.BioData mining · 2026Article
- From metabolic substrate to epigenetic regulation: roles and mechanisms of lactylation in brain health and disease.Frontiers in molecular neuroscience · 2026Review
- Machine learning-driven prediction of substrates for enzymes introducing or removing protein post-translational modifications.Communications chemistry · 2025Article
- A deep learning framework for lysine 2-hydroxyisobutyrylation site prediction using evolutionary feature representation.Scientific reports · 2025Article
- Deep learning-assisted tools to understand the structural biology of the synapse.Biomedical engineering letters · 2025Review
- Beyond evolution:Biosafety and health · 2025Review
- Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases.Scientific reports · 2025Article
- Substrate prediction for RiPP biosynthetic enzymesDigital discovery · 2025Article
- IL-33/ST2 axis in diverse diseases: regulatory mechanisms and therapeutic potential.Frontiers in immunology · 2025Review
- SUMO-LMNet: Lossless mapping network for predicting SUMOylation sites in SUMO1 and SUMO2 using high-dimensional features.Computational and structural biotechnology journal · 2025Article
- iKcr-DRC: prediction of lysine crotonylation sites in proteins based on a novel attention module and DenseNet.Frontiers in genetics · 2025Article
- Post-translational modifications of proteins in cardiovascular diseases examined by proteomic approaches.The FEBS journal · 2025Review
- Large Language Model (LLM)-Based Advances in Prediction of Post-translational Modification Sites in Proteins.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Artificial Intelligence Transforming Post-Translational Modification Research.Bioengineering (Basel, Switzerland) · 2024Review
- Human essential gene identification based on feature fusion and feature screening.IET systems biology · 2024Article
- Construction of machine learning diagnostic models for cardiovascular pan-disease based on blood routine and biochemical detection data.Cardiovascular diabetology · 2024Article
- Nphos: Database and Predictor of Protein N-phosphorylation.Genomics, proteomics & bioinformatics · 2024Article
- Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model.Nature communications · 2024Article
- TransPTM: a transformer-based model for non-histone acetylation site prediction.Briefings in bioinformatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Post-translational modifications (PTMs) are closely linked to numerous diseases, playing a significant role in regulating protein structures, activities, and functions. Therefore, the identification of PTMs is crucial for understanding the mechanisms of cell biology and diseases therapy. Compared to traditional machine learning methods, the deep learning approaches for PTM prediction provide accurate and rapid screening, guiding the downstream wet experiments to leverage the screened information for focused studies. In this paper, we reviewed the recent works in deep learning to identify phosphorylation, acetylation, ubiquitination, and other PTM types. In addition, we summarized PTM databases and discussed future directions with critical insights.
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What Socratic holds
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.