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Current Proteomics

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ISSN (Print): 1570-1646
ISSN (Online): 1875-6247

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Research Article

Identifying Ligand-receptor Interactions via an Integrated Fuzzy Model

Author(s): Chang Xu, Yijie Ding, Limin Jiang, Cong Shen, Gaoyan Zhang* and Xuyao Yu*

Volume 17, Issue 4, 2020

Page: [287 - 301] Pages: 15

DOI: 10.2174/1570164616666190306151423

Price: $65

Abstract

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.

Keywords: Ligand-receptor interactions, feature extraction, substitution matrix representation, discrete cosine transform, support vector machine, source code.

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