ArticleBioMed research international2016
RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- DCPPS: Prediction of Kinase-Specific Phosphorylation Sites Using Dynamic Embedding and Cross-Representation Interaction.Interdisciplinary sciences, computational life sciences · 2025Article
- CaLMPhosKAN: prediction of general phosphorylation sites in proteins via fusion of codon aware embeddings with amino acid aware embeddings and wavelet-based Kolmogorov-Arnold network.Bioinformatics (Oxford, England) · 2025Article
- LMPTMSite: A Platform for PTM Site Prediction in Proteins Leveraging Transformer-Based Protein Language Models.Methods in molecular biology (Clifton, N.J.) · 2025Article
- From ketogenic metabolism to targeted therapeutics: current advances in β-hydroxybutyrylation.Frontiers in immunology · 2025Review
- A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction.Genomics, proteomics & bioinformatics · 2023Review
- PhosBoost: Improved phosphorylation prediction recall using gradient boosting and protein language models.Plant direct · 2023Article
- Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach.Briefings in bioinformatics · 2023Article
- IPs-GRUAtt: An attention-based bidirectional gated recurrent unit network for predicting phosphorylation sites of SARS-CoV-2 infection.Molecular therapy. Nucleic acids · 2023Article
- A Transfer-Learning-Based Deep Convolutional Neural Network for Predicting Leukemia-Related Phosphorylation Sites from Protein Primary Sequences.International journal of molecular sciences · 2022Article
- FEPS: A Tool for Feature Extraction from Protein Sequence.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A Random Forest-Based Predictor and Classifier for Prxs.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites.Scientific reports · 2021Article
- DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites.Frontiers in cell and developmental biology · 2021Article
- GasPhos: Protein Phosphorylation Site Prediction Using a New Feature Selection Approach with a GA-Aided Ant Colony System.International journal of molecular sciences · 2020Article
- Hidden dynamic signatures drive substrate selectivity in the disordered phosphoproteome.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- DeepKinZero: zero-shot learning for predicting kinase-phosphosite associations involving understudied kinases.Bioinformatics (Oxford, England) · 2020Article
- RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites.Computational and structural biotechnology journal · 2020Article
- Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies.Journal of computer-aided molecular design · 2019Article
- iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC.Molecular biology reports · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Protein phosphorylation is one of the most widespread regulatory mechanisms in eukaryotes. Over the past decade, phosphorylation site prediction has emerged as an important problem in the field of bioinformatics. Here, we report a new method, termed Random Forest-based Phosphosite predictor 2.0 (RF-Phos 2.0), to predict phosphorylation sites given only the primary amino acid sequence of a protein as input. RF-Phos 2.0, which uses random forest with sequence and structural features, is able to identify putative sites of phosphorylation across many protein families. In side-by-side comparisons based on 10-fold cross validation and an independent dataset, RF-Phos 2.0 compares favorably to other popular mammalian phosphosite prediction methods, such as PhosphoSVM, GPS2.1, and Musite.
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Registered trials
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