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- W4313216105 endingPage "106446" @default.
- W4313216105 startingPage "106446" @default.
- W4313216105 abstract "New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies." @default.
- W4313216105 created "2023-01-06" @default.
- W4313216105 creator A5004326219 @default.
- W4313216105 creator A5011756049 @default.
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- W4313216105 creator A5016293661 @default.
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- W4313216105 creator A5061697130 @default.
- W4313216105 creator A5080045714 @default.
- W4313216105 creator A5086001755 @default.
- W4313216105 date "2023-03-01" @default.
- W4313216105 modified "2023-09-27" @default.
- W4313216105 title "A systematic review of state-of-the-art strategies for machine learning-based protein function prediction" @default.
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