Exploring local discriminative information from evolutionary profiles for cytokine-receptor interaction prediction

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.