Chang Xu, Yijie Ding, Limin Jiang, Cong Shen, Gaoyan Zhang* and Xuyao Yu* Pages 287 - 301 ( 15 )
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. git.
Ligand-receptor interactions, feature extraction, substitution matrix representation, discrete cosine transform, support vector machine, source code.
School of Computer Science and Technology, Tianjin University, Tianjin, School of Electronic and Information Engineering, Suzhou University, Suzhou, School of Computer Science and Technology, Tianjin University, Tianjin, School of Computer Science and Technology, Tianjin University, Tianjin, School of Computer Science and Technology, Tianjin University, Tianjin, Department of Radiotherapy, Tianjin Medical University, Cancer Institute and Hospital, Tianjin