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Current Proteomics

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

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Research Article

An Integrated Prediction Method for Identifying Protein-Protein Interactions

Author(s): Chang Xu, Limin Jiang, Zehua Zhang, Xuyao Yu, Renhai Chen* and Junhai Xu*

Volume 17, Issue 4, 2020

Page: [271 - 286] Pages: 16

DOI: 10.2174/1570164616666190306152318

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Abstract

Background: Protein-Protein Interactions (PPIs) play a key role in various biological processes. Many methods have been developed to predict protein-protein interactions and protein interaction networks. However, many existing applications are limited, because of relying on a large number of homology proteins and interaction marks.

Methods: In this paper, we propose a novel integrated learning approach (RF-Ada-DF) with the sequence-based feature representation, for identifying protein-protein interactions. Our method firstly constructs a sequence-based feature vector to represent each pair of proteins, via Multivariate Mutual Information (MMI) and Normalized Moreau-Broto Autocorrelation (NMBAC). Then, we feed the 638- dimentional features into an integrated learning model for judging interaction pairs and non-interaction pairs. Furthermore, this integrated model embeds Random Forest in AdaBoost framework and turns weak classifiers into a single strong classifier. Meanwhile, we also employ double fault detection in order to suppress over-adaptation during the training process.

Results: To evaluate the performance of our method, we conduct several comprehensive tests for PPIs prediction. On the H. pylori dataset, our method achieves 88.16% accuracy and 87.68% sensitivity, the accuracy of our method is increased by 0.57%. On the S. cerevisiae dataset, our method achieves 95.77% accuracy and 93.36% sensitivity, the accuracy of our method is increased by 0.76%. On the Human dataset, our method achieves 98.16% accuracy and 96.80% sensitivity, the accuracy of our method is increased by 0.6%. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. The datasets and codes are available at https://github.com/guofei-tju/RF-Ada-DF.git.

Keywords: Protein-protein interaction, multivariate mutual information, random forest, AdaBoost framework, double fault detection, sensitivity.

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