A novel machine learning method for cytokine-receptor interaction prediction
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.
Keywords: Cytokine–receptor interaction prediction; feature extraction; random forest; sequence evolutional information
Document Type: Research Article
Publication date: 01 February 2016
- Combinatorial Chemistry & High Throughput Screening publishes full length original research articles and reviews describing various topics in combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) and/or high throughput screening (e.g. developmental, practical or theoretical). Ancillary subjects of key importance, such as robotics and informatics, will also be covered by the journal. In these respective subject areas, Combinatorial Chemistry & High Throughput Screening is intended to function as the most comprehensive and up-to-date medium available. The journal should be of value to individuals engaged in the process of drug discoveryand development, in the settings of industry, academia or government.
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