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- W2019898130 abstract "The support vector machine approach was introduced to predict the beta-turns in proteins. The overall self-consistency rate by the re-substitution test for the training or learning dataset reached 100%. Both the training dataset and independent testing dataset were taken from Chou [J. Pept. Res. 49 (1997) 120]. The success prediction rates by the jackknife test for the beta-turn subset of 455 tetrapeptides and non-beta-turn subset of 3807 tetrapeptides in the training dataset were 58.1 and 98.4%, respectively. The success rates with the independent dataset test for the beta-turn subset of 110 tetrapeptides and non-beta-turn subset of 30,231 tetrapeptides were 69.1 and 97.3%, respectively. The results obtained from this study support the conclusion that the residue-coupled effect along a tetrapeptide is important for the formation of a beta-turn." @default.
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- W2019898130 date "2003-05-01" @default.
- W2019898130 modified "2023-09-29" @default.
- W2019898130 title "Prediction of β-turns with learning machines" @default.
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- W2019898130 doi "https://doi.org/10.1016/s0196-9781(03)00133-5" @default.
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