A Novel Machine Learning Method for Cytokine-Receptor Interaction Prediction

作者：Wei, Leyi; Zou, Quan; Liao, Minghong; Lu, Huijuan; Zhao, Yuming^*

来源：Combinatorial Chemistry & High Throughput Screening, 2016, 19(2): 144-152.

DOI：10.2174/1386207319666151110122621

• Cytokine-receptor interaction prediction
• feature extraction
• random forest
• sequence evolutional information

Most essential functions are associated with various protein-protein interactions, particularly the cytokine-receptor interaction. Knowledge of the heterogeneous network of cytokinereceptor 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.

• 出版日期2016
• 单位天津大学; 厦门大学; 东北林业大学; 中国计量大学