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• W2543374098 abstract "Studies on phosphorylation are important but challenging for both wet-bench experiments and computational studies, and accurate non-kinase-specific prediction tools are highly desirable for whole-genome annotation in a wide variety of species. Here, we describe a phosphorylation site prediction webserver, PhosphoSVM, that employs Support Vector Machine to combine protein secondary structure information and seven other one-dimensional structural properties, including Shannon entropy, relative entropy, predicted protein disorder information, predicted solvent accessible area, amino acid overlapping properties, averaged cumulative hydrophobicity, and subsequence k-nearest neighbor profiles. This method achieved AUC values of 0.8405/0.8183/0.7383 for serine (S), threonine (T), and tyrosine (Y) phosphorylation sites, respectively, in animals with a tenfold cross-validation. The model trained by the animal phosphorylation sites was also applied to a plant phosphorylation site dataset as an independent test. The AUC values for the independent test data set were 0.7761/0.6652/0.5958 for S/T/Y phosphorylation sites, respectively. This algorithm with the optimally trained model was implemented as a webserver. The webserver, trained model, and all datasets used in the current study are available at http://sysbio.unl.edu/PhosphoSVM ." @default.
• W2543374098 created "2016-11-04" @default.
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• W2543374098 date "2016-10-28" @default.
• W2543374098 modified "2023-09-27" @default.
• W2543374098 title "Prediction of Protein Phosphorylation Sites by Integrating Secondary Structure Information and Other One-Dimensional Structural Properties" @default.
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• W2543374098 doi "https://doi.org/10.1007/978-1-4939-6406-2_18" @default.
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