Most essential functions are associated with various protein–protein interactions,
particularly the cytokine–receptor interaction. Knowledge of the heterogeneous network of cytokine–
receptor interactions provides insights into various human physiological functions. However, only a few studies are
focused on the computational prediction of these interactions. In this study, we propose a novel machine-learning-based
method for predicting cytokine–receptor interactions. A protein sequence is first transformed by incorporating the
sequence evolutional information and then formulated with the following three aspects: (1) the k-skip-n-gram model, (2)
physicochemical properties, and (3) local pseudo position-specific score matrix (local PsePSSM). The random forest
classifier is subsequently employed to predict potential cytokine–receptor interactions. Experimental results on a dataset
of Homo sapiens show that the proposed method exhibits improved performance, with 3.4% higher overall prediction
accuracy, than existing methods.
Keywords: Cytokine–receptor interaction prediction, feature extraction, random forest, sequence evolutional information.
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