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- W4296750899 endingPage "e1069" @default.
- W4296750899 startingPage "e1069" @default.
- W4296750899 abstract "N-linked is the most common type of glycosylation which plays a significant role in identifying various diseases such as type I diabetes and cancer and helps in drug development. Most of the proteins cannot perform their biological and psychological functionalities without undergoing such modification. Therefore, it is essential to identify such sites by computational techniques because of experimental limitations. This study aims to analyze and synthesize the progress to discover N-linked places using machine learning methods. It also explores the performance of currently available tools to predict such sites. Almost seventy research articles published in recognized journals of the N-linked glycosylation field have shortlisted after the rigorous filtering process. The findings of the studies have been reported based on multiple aspects: publication channel, feature set construction method, training algorithm, and performance evaluation. Moreover, a literature survey has developed a taxonomy of N-linked sequence identification. Our study focuses on the performance evaluation criteria, and the importance of N-linked glycosylation motivates us to discover resources that use computational methods instead of the experimental method due to its limitations." @default.
- W4296750899 created "2022-09-23" @default.
- W4296750899 creator A5011300632 @default.
- W4296750899 creator A5050111038 @default.
- W4296750899 creator A5051481734 @default.
- W4296750899 creator A5052860906 @default.
- W4296750899 creator A5080026682 @default.
- W4296750899 date "2022-09-21" @default.
- W4296750899 modified "2023-10-14" @default.
- W4296750899 title "An analytical study on the identification of N-linked glycosylation sites using machine learning model" @default.
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- W4296750899 doi "https://doi.org/10.7717/peerj-cs.1069" @default.