ArticleInternational journal of molecular sciences2020
GasPhos: Protein Phosphorylation Site Prediction Using a New Feature Selection Approach with a GA-Aided Ant Colony System.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 13 citations in OpenAlex.
- Article
- Analysis of phosphomotifs coupled to phosphoproteome and interactome unveils potential human kinase substrate proteins in SARS-CoV-2.Frontiers in cellular and infection microbiology · 2025Article
- Uncovering human kinase substrates in nipah proteome.Frontiers in bioinformatics · 2025Article
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction.Genomics, proteomics & bioinformatics · 2023Review
- Editorial of Special Issue "Protein Post-Translational Modifications in Signal Transduction and Diseases".International journal of molecular sciences · 2021Article
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Authors and funding
5 authors at 1 institution in 1 country.
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Abstract
Protein phosphorylation is one of the most important post-translational modifications, and many biological processes are related to phosphorylation, such as DNA repair, transcriptional regulation and signal transduction and, therefore, abnormal regulation of phosphorylation usually causes diseases. If we can accurately predict human phosphorylation sites, this could help to solve human diseases. Therefore, we developed a kinase-specific phosphorylation prediction system, GasPhos, and proposed a new feature selection approach, called Gas, based on the ant colony system and a genetic algorithm and used performance evaluation strategies focused on different kinases to choose the best learning model. Gas uses the mean decrease Gini index (MDGI) as a heuristic value for path selection and adopts binary transformation strategies and new state transition rules. GasPhos can predict phosphorylation sites for six kinases and showed better performance than other phosphorylation prediction tools. The disease-related phosphorylated proteins that were predicted with GasPhos are also discussed. Finally, Gas can be applied to other issues that require feature selection, which could help to improve prediction performance. GasPhos is available at http://predictor.nchu.edu.tw/GasPhos.
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