title: A novel machine learning method for cytokine-receptor interaction prediction.
abstract: 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.
authors.forename: Leyi
authors.initials: L
authors.lastname: Wei
authors.forename: Quan
authors.initials: Q
authors.lastname: Zou
authors.forename: Minghong
authors.initials: M
authors.lastname: Liao
authors.forename: Huijuan
authors.initials: H
authors.lastname: Lu
authors.affiliation: Information and Computer Engineering College, Northeast Forestry University, Harbin, Heilongjiang, P.R.China. zymyoyo@hotmail.com.
authors.forename: Yuming
authors.initials: Y
authors.lastname: Zhao
mesh_terms: D016207:Cytokines / Q000737:chemistry* / Q000378:metabolism; D030562:Databases, Protein; D057166:High-Throughput Screening Assays; D006801:Humans; D000069550:Machine Learning; D011485:Protein Binding; D018121:Receptors, Cytokine / Q000737:chemistry* / Q000378:metabolism
chemical_list: D016207:Cytokines; D018121:Receptors, Cytokine