A novel machine learning method for cytokine-receptor interaction prediction

Title:A novel machine learning method for cytokine-receptor interaction prediction

Volume: 19 Issue: 2

Author(s): Leyi Wei, Quan Zou, Minghong Liao, Huijuan Lu and Yuming Zhao

Affiliation:

Keywords: Cytokine–receptor interaction prediction, feature extraction, random forest, sequence evolutional information.

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.

Cite this article as:

Wei Leyi, Zou Quan, Liao Minghong, Lu Huijuan and Zhao Yuming, A novel machine learning method for cytokine-receptor interaction prediction, Combinatorial Chemistry & High Throughput Screening 2016; 19 (2) . https://dx.doi.org/10.2174/1386207319666151110122621