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• W4310568385 abstract "RNA molecules carry out various cellular functions, and understanding the mechanisms behind their functions requires the knowledge of their 3D structures. Different types of computational methods have been developed to model RNA 3D structures over the past decade. These methods were widely used by researchers although their performance needs to be further improved. Recently, along with these traditional methods, machine-learning techniques have been increasingly applied to RNA 3D structure prediction and show significant improvement in performance. Here we shall give a brief review of the traditional methods and recent related advances in machine-learning approaches for RNA 3D structure prediction." @default.
• W4310568385 created "2022-12-12" @default.
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• W4310568385 date "2022-11-30" @default.
• W4310568385 modified "2023-10-11" @default.
• W4310568385 title "Advances in RNA 3D Structure Prediction" @default.
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• W4310568385 doi "https://doi.org/10.1021/acs.jcim.2c00939" @default.
• W4310568385 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36451090" @default.
• W4310568385 hasPublicationYear "2022" @default.
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