ArticleFrontiers in genetics2022
Ensemble learning-based feature selection for phosphorylation site detection.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Empirical Comparison and Analysis of Artificial Intelligence-Based Methods for Identifying Phosphorylation Sites of SARS-CoV-2 Infection.International journal of molecular sciences · 2024Review
- PhosBoost: Improved phosphorylation prediction recall using gradient boosting and protein language models.Plant direct · 2023Article
- Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery.Proteomes · 2023Review
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Authors and funding
4 authors.
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Abstract
SARS-COV-2 is prevalent all over the world, causing more than six million deaths and seriously affecting human health. At present, there is no specific drug against SARS-COV-2. Protein phosphorylation is an important way to understand the mechanism of SARS -COV-2 infection. It is often expensive and time-consuming to identify phosphorylation sites with specific modified residues through experiments. A method that uses machine learning to make predictions about them is proposed. As all the methods of extracting protein sequence features are knowledge-driven, these features may not be effective for detecting phosphorylation sites without a complete understanding of the mechanism of protein. Moreover, redundant features also have a great impact on the fitting degree of the model. To solve these problems, we propose a feature selection method based on ensemble learning, which firstly extracts protein sequence features based on knowledge, then quantifies the importance score of each feature based on data, and finally uses the subset of important features as the final features to predict phosphorylation sites.
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