Abstract
Background: Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics.
Objective: In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. Results: SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. Conclusion: This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress.Keywords: Ensemble learning algorithm, Escherichia coli, machine learning, Protein-Protein Interactions (PPI), random forest (RF), support vector machine (SVM).
Medicinal Chemistry
Title:Application of Machine Learning Approaches for Protein-protein Interactions Prediction
Volume: 13 Issue: 6
Author(s): Mengying Zhang, Qiang Su, Yi Lu, Manman Zhao and Bing Niu*
Affiliation:
- College of Life Science, Shanghai University, 99 Shang-Da Road, Shanghai 200444,China
Keywords: Ensemble learning algorithm, Escherichia coli, machine learning, Protein-Protein Interactions (PPI), random forest (RF), support vector machine (SVM).
Abstract: Background: Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics.
Objective: In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. Results: SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. Conclusion: This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress.Export Options
About this article
Cite this article as:
Zhang Mengying , Su Qiang , Lu Yi , Zhao Manman and Niu Bing*, Application of Machine Learning Approaches for Protein-protein Interactions Prediction, Medicinal Chemistry 2017; 13 (6) . https://dx.doi.org/10.2174/1573406413666170522150940
DOI https://dx.doi.org/10.2174/1573406413666170522150940 |
Print ISSN 1573-4064 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6638 |
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