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• W4313334552 startingPage "706" @default.
• W4313334552 abstract "CRISPR-Cas, as a tool for gene editing, has received extensive attention in recent years. Anti-CRISPR (Acr) proteins can inactivate the CRISPR-Cas defense system during interference phase, and can be used as a potential tool for the regulation of gene editing. In-depth study of Anti-CRISPR proteins is of great significance for the implementation of gene editing. In this study, we developed a high-accuracy prediction model based on two-step model fusion strategy, called AcrPred, which could produce an AUC of 0.952 with independent dataset validation. To further validate the proposed model, we compared with published tools and correctly identified 9 of 10 new Acr proteins, indicating the strong generalization ability of our model. Finally, for the convenience of related wet-experimental researchers, a user-friendly web-server AcrPred (Anti-CRISPR proteins Prediction) was established at http://lin-group.cn/server/AcrPred, by which users can easily identify potential Anti-CRISPR proteins." @default.
• W4313334552 created "2023-01-06" @default.
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• W4313334552 date "2023-02-01" @default.
• W4313334552 modified "2023-10-16" @default.
• W4313334552 title "AcrPred: A hybrid optimization with enumerated machine learning algorithm to predict Anti-CRISPR proteins" @default.
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• W4313334552 doi "https://doi.org/10.1016/j.ijbiomac.2022.12.250" @default.
• W4313334552 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36584777" @default.
• W4313334552 hasPublicationYear "2023" @default.
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