Background: The ligand-receptor interaction plays an important role in signal transduction
required for cellular differentiation, proliferation, and immune response process. The analysis of
ligand-receptor interactions is helpful to provide a deeper understanding of cellular proliferation/
differentiation and other cell processes.
Methods: The computational technique would be used to promote ligand-receptor interactions research
in future proteomics research. In this paper, we propose a novel computational method to predict
ligand-receptor interactions from amino acid sequences by a machine learning approach. We extract
features from ligand and receptor sequences by Histogram of Oriented Gradient (HOG) and Discrete
Cosine Transform (DCT). Then, these features are fed into the Fuzzy C-Means (FCM) clustering
algorithm for clustering, and also we get multiple training subsets to generate the same number of
sub-classifiers. We choose an optimal sub-classifier for predicting ligand-receptor interactions according
to the similarity from one sample to training subsets.
Observations: In order to verify the performance, we perform five-fold cross-validation experiments
on a ligand-receptor interactions dataset and achieve 80.08% accuracy, 82.98% sensitivity and 80.02%
specificity. Then, we test our extracted feature method on two Protein-Protein Interactions (PPIs)
datasets, and achieve accuracies of 93.79% and 87.46%, respectively.
Conclusion: Our proposed method can be a useful tool for identifying of ligand-receptor interactions.
Related data sets and source code are available at https://github.com/guofei-tju/ligand-receptorinteractions.