ArticleInternational journal of molecular sciences2022
TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture.
Article in International journal of molecular sciences, 2022. 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.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Bioinformatics analysis of the sodium-dependent neurotransmitter transporter family in Bursaphelenchus xylophilus.BMC genomics · 2026Article
- CMV-GLA: contrastive multi-view graph layer attention for predicting phosphorylation site-disease associations.BMC bioinformatics · 2026Article
- DeepPTMPred: a multi-modal deep learning framework for accurate prediction of protein post-translational modification sites.Briefings in bioinformatics · 2026Article
- Post-translational Modifications in Proteins: Prediction Methods, Biological Functions, and Diseases.MedComm · 2026Review
- Optimized deep intelligent parameter efficient fine-tuning protein language model system for predicting dephosphorylation site.Journal of molecular modeling · 2026Article
- Explainable deep learning and radiomics integration for differentiating insulinomas from NF-PNETs in EUS imaging.BMC medical informatics and decision making · 2026Article
- ProtPSP: Determination of Protein Phosphorylation Sites with the Protein Large Language Model.ACS omega · 2025Article
- PhosF3C: a feature fusion architecture with fine-tuned protein language model and conformer for prediction of general phosphorylation site.Briefings in bioinformatics · 2025Article
- An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.Scientific reports · 2025Article
- Large Language Model (LLM)-Based Advances in Prediction of Post-translational Modification Sites in Proteins.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Advances in Prediction of Posttranslational Modification Sites Known to Localize in Protein Supersecondary Structures.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network.Interdisciplinary sciences, computational life sciences · 2024Article
- Attenphos: General Phosphorylation Site Prediction Model Based on Attention Mechanism.International journal of molecular sciences · 2024Article
- PhosBoost: Improved phosphorylation prediction recall using gradient boosting and protein language models.Plant direct · 2023Article
- Integrating Multiple Single-Cell RNA Sequencing Datasets Using Adversarial Autoencoders.International journal of molecular sciences · 2023Article
- PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning.International journal of molecular sciences · 2023Article
- Identification of phosphorylation site using S-padding strategy based convolutional neural network.Health information science and systems · 2022Article
- CAT-CPI: Combining CNN and transformer to learn compound image features for predicting compound-protein interactions.Frontiers in molecular biosciences · 2022Article
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6 authors.
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Abstract
Protein phosphorylation is one of the most critical post-translational modifications of proteins in eukaryotes, which is essential for a variety of biological processes. Plenty of attempts have been made to improve the performance of computational predictors for phosphorylation site prediction. However, most of them are based on extra domain knowledge or feature selection. In this article, we present a novel deep learning-based predictor, named TransPhos, which is constructed using a transformer encoder and densely connected convolutional neural network blocks, for predicting phosphorylation sites. Data experiments are conducted on the datasets of PPA (version 3.0) and Phospho. ELM. The experimental results show that our TransPhos performs better than several deep learning models, including Convolutional Neural Networks (CNN), Long-term and short-term memory networks (LSTM), Recurrent neural networks (RNN) and Fully connected neural networks (FCNN), and some state-of-the-art deep learning-based prediction tools, including GPS2.1, NetPhos, PPRED, Musite, PhosphoSVM, SKIPHOS, and DeepPhos. Our model achieves a good performance on the training datasets of Serine (S), Threonine (T), and Tyrosine (Y), with AUC values of 0.8579, 0.8335, and 0.6953 using 10-fold cross-validation tests, respectively, and demonstrates that the presented TransPhos tool considerably outperforms competing predictors in general protein phosphorylation site prediction.
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