Journal
COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING
Volume 19, Issue 2, Pages 144-152Publisher
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/1386207319666151110122621
Keywords
Cytokine-receptor interaction prediction; feature extraction; random forest; sequence evolutional information
Funding
- Natural Science Foundation of Heilongjiang Province [F201132]
- State Key Laboratory of Tree Genetics and Breeding (Northeast Forestry University) [201207]
- Natural Science Foundation of China [61370010, 61272315]
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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.
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