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

Wei L,Zou Q,Liao M,Lu H,Zhao Y

doi

10.2174/1386207319666151110122621

subject

Has Abstract

pub_date

2016-01-01 00:00:00

pages

144-52

issue

2

eissn

1386-2073

issn

1875-5402

pii

CCHTS-EPUB-71685

journal_volume

19

pub_type

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