Analysis of protein phosphorylation site predictors with an independent dataset
International Journal of Bioinformatics Research and Applications
Probabilistic prediction of protein phosphorylation sites using kernel machines
Proceedings of the 27th Annual ACM Symposium on Applied Computing
ACM SIGAPP Applied Computing Review
An ensemble learning approach for prediction of phosphorylation sites
International Journal of Bioinformatics Research and Applications
| Hi-index | 3.84 |
Summary: We here present a neural network-based method for the prediction of protein phosphorylation sites in yeast—an important model organism for basic research. Existing protein phosphorylation site predictors are primarily based on mammalian data and show reduced sensitivity on yeast phosphorylation sites compared to those in humans, suggesting the need for an yeast-specific phosphorylation site predictor. NetPhosYeast achieves a correlation coefficient close to 0.75 with a sensitivity of 0.84 and specificity of 0.90 and outperforms existing predictors in the identification of phosphorylation sites in yeast. Availability: The NetPhosYeast prediction service is available as a public web server at http://www.cbs.dtu.dk/services/NetPhosYeast/ Contact: nikob@cbs.dtu.dk