Cytokine–receptor interaction is one of the most important types of protein–protein interactions that are widely involved in cellular regulatory processes. Knowledge of cytokine–receptor interactions facilitates to deeply understand several physiological functions. In post-genomic era of sequence explosion, there is an increasing demand for developing machine learning based computational methods for the fast and accurate cytokine–receptor interaction prediction. However, the major problem lying on existing machine learning based methods is that the overall prediction accuracy is relatively low. To improve the accuracy, a crucial step is to establish a well-defined feature representation algorithm. Motivated on this perspective, we propose a novel feature representation method by integrating local information embedded in evolutionary profiles with the  Pse-PSSM  and  AAC-PSSM-AC  feature models. We further develop an improved prediction method, namely CRI-Pred, based on the proposed feature set using the Random Forest classifier. Experimental results evaluated with the jackknife test show that the CRI-Pred predictor outperforms the state-of-the-art methods, 5.1% higher in terms of the overall accuracy. This indicates the effectiveness and superiority of CRI-Pred. A webserver that implements CRI-Pred is now freely available at   http://server.malab.cn/CRIPred/Index.html   to the public to use in practical applications.
['Leyi Wei', 'Zhang Bowen', 'Chen Zhiyong', 'Xing Gao', 'Minghong Liao']
Exploring local discriminative information from evolutionary profiles for cytokine–receptor interaction prediction