PhosPred-RF: A Novel Sequence-Based Predictor for Phosphorylation Sites Using Sequential Information Only

Many recent efforts have been made for the development of machine learning-based methods for fast and accurate phosphorylation site prediction. Currently, a majority of well-performing methods are based on hybrid information to build prediction models, such as evolutionary information, disorder information, and so on. Unfortunately, this type of methods suffers two major limitations: one is that it would not be much of help for protein phosphorylation site prediction in case of no obvious homology detected; the other is that computing such the complicated information is time-consuming, which probably limits the usage of predictors in practical applications. In this paper, we present a simple, fast, and powerful feature representation algorithm, which sufficiently explores the sequential information from multiple perspectives only based on primary sequences, and successfully captures the differences between true phosphorylation sites and hboxnon-phosphorylation sites. Using the proposed features, we propose a random forest-based predictor named PhosPred-RF in the prediction of protein phosphorylation sites from proteins. We evaluate and compare the proposed predictor with the state-of-the-art predictors on some benchmark data sets. The experimental results show that PhosPred-RF outperforms other existing predictors, demonstrating its potential to be a useful tool for protein phosphorylation site prediction. Currently, the proposed PhosPred-RF is freely accessible to the public through the user-friendly webserver http://server.malab.cn/PhosPred-RF.

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