Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network
Protein-protein interactions (PPIs) play an important role in most of the biological processes. How to correctly and efficiently detect protein interaction is a problem that is worth studying. Although high-throughput technologies provide the possibility to detect large-scale PPIs, these cannot be u...
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Published in | Molecular bioSystems Vol. 13; no. 7; pp. 1336 - 1344 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
England
27.06.2017
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Subjects | |
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Abstract | Protein-protein interactions (PPIs) play an important role in most of the biological processes. How to correctly and efficiently detect protein interaction is a problem that is worth studying. Although high-throughput technologies provide the possibility to detect large-scale PPIs, these cannot be used to detect whole PPIs, and unreliable data may be generated. To solve this problem, in this study, a novel computational method was proposed to effectively predict the PPIs using the information of a protein sequence. The present method adopts Zernike moments to extract the protein sequence feature from a position specific scoring matrix (PSSM). Then, these extracted features were reconstructed using the stacked autoencoder. Finally, a novel probabilistic classification vector machine (PCVM) classifier was employed to predict the protein-protein interactions. When performed on the PPIs datasets of
Yeast
and
H. pylori
, the proposed method could achieve average accuracies of 96.60% and 91.19%, respectively. The promising result shows that the proposed method has a better ability to detect PPIs than other detection methods. The proposed method was also applied to predict PPIs on other species, and promising results were obtained. To evaluate the ability of our method, we compared it with the-state-of-the-art support vector machine (SVM) classifier for the
Yeast
dataset. The results obtained
via
multiple experiments prove that our method is powerful, efficient, feasible, and make a great contribution to proteomics research.
Protein-protein interactions (PPIs) play an important role in most of the biological processes. |
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AbstractList | Protein-protein interactions (PPIs) play an important role in most of the biological processes. How to correctly and efficiently detect protein interaction is a problem that is worth studying. Although high-throughput technologies provide the possibility to detect large-scale PPIs, these cannot be used to detect whole PPIs, and unreliable data may be generated. To solve this problem, in this study, a novel computational method was proposed to effectively predict the PPIs using the information of a protein sequence. The present method adopts Zernike moments to extract the protein sequence feature from a position specific scoring matrix (PSSM). Then, these extracted features were reconstructed using the stacked autoencoder. Finally, a novel probabilistic classification vector machine (PCVM) classifier was employed to predict the protein-protein interactions. When performed on the PPIs datasets of Yeast and H. pylori, the proposed method could achieve average accuracies of 96.60% and 91.19%, respectively. The promising result shows that the proposed method has a better ability to detect PPIs than other detection methods. The proposed method was also applied to predict PPIs on other species, and promising results were obtained. To evaluate the ability of our method, we compared it with the-state-of-the-art support vector machine (SVM) classifier for the Yeast dataset. The results obtained via multiple experiments prove that our method is powerful, efficient, feasible, and make a great contribution to proteomics research. Protein-protein interactions (PPIs) play an important role in most of the biological processes. How to correctly and efficiently detect protein interaction is a problem that is worth studying. Although high-throughput technologies provide the possibility to detect large-scale PPIs, these cannot be used to detect whole PPIs, and unreliable data may be generated. To solve this problem, in this study, a novel computational method was proposed to effectively predict the PPIs using the information of a protein sequence. The present method adopts Zernike moments to extract the protein sequence feature from a position specific scoring matrix (PSSM). Then, these extracted features were reconstructed using the stacked autoencoder. Finally, a novel probabilistic classification vector machine (PCVM) classifier was employed to predict the protein-protein interactions. When performed on the PPIs datasets of Yeast and H. pylori , the proposed method could achieve average accuracies of 96.60% and 91.19%, respectively. The promising result shows that the proposed method has a better ability to detect PPIs than other detection methods. The proposed method was also applied to predict PPIs on other species, and promising results were obtained. To evaluate the ability of our method, we compared it with the-state-of-the-art support vector machine (SVM) classifier for the Yeast dataset. The results obtained via multiple experiments prove that our method is powerful, efficient, feasible, and make a great contribution to proteomics research. Protein-protein interactions (PPIs) play an important role in most of the biological processes. Protein–protein interactions (PPIs) play an important role in most of the biological processes. How to correctly and efficiently detect protein interaction is a problem that is worth studying. Although high-throughput technologies provide the possibility to detect large-scale PPIs, these cannot be used to detect whole PPIs, and unreliable data may be generated. To solve this problem, in this study, a novel computational method was proposed to effectively predict the PPIs using the information of a protein sequence. The present method adopts Zernike moments to extract the protein sequence feature from a position specific scoring matrix (PSSM). Then, these extracted features were reconstructed using the stacked autoencoder. Finally, a novel probabilistic classification vector machine (PCVM) classifier was employed to predict the protein–protein interactions. When performed on the PPIs datasets of Yeast and H. pylori , the proposed method could achieve average accuracies of 96.60% and 91.19%, respectively. The promising result shows that the proposed method has a better ability to detect PPIs than other detection methods. The proposed method was also applied to predict PPIs on other species, and promising results were obtained. To evaluate the ability of our method, we compared it with the-state-of-the-art support vector machine (SVM) classifier for the Yeast dataset. The results obtained via multiple experiments prove that our method is powerful, efficient, feasible, and make a great contribution to proteomics research. |
Author | You, Zhu-Hong Chen, Xing Zhou, Xi Li, Xiao Wang, Lei Jiang, Tong-Hai Wang, Yan-Bin |
AuthorAffiliation | School of Information and Electrical Engineering Xinjiang Technical Institutes of Physics and Chemistry China University of Mining and Technology Chinese Academy of Science |
AuthorAffiliation_xml | – name: Xinjiang Technical Institutes of Physics and Chemistry – name: School of Information and Electrical Engineering – name: China University of Mining and Technology – name: Chinese Academy of Science |
Author_xml | – sequence: 1 givenname: Yan-Bin surname: Wang fullname: Wang, Yan-Bin – sequence: 2 givenname: Zhu-Hong surname: You fullname: You, Zhu-Hong – sequence: 3 givenname: Xiao surname: Li fullname: Li, Xiao – sequence: 4 givenname: Tong-Hai surname: Jiang fullname: Jiang, Tong-Hai – sequence: 5 givenname: Xing surname: Chen fullname: Chen, Xing – sequence: 6 givenname: Xi surname: Zhou fullname: Zhou, Xi – sequence: 7 givenname: Lei surname: Wang fullname: Wang, Lei |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28604872$$D View this record in MEDLINE/PubMed |
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Snippet | Protein-protein interactions (PPIs) play an important role in most of the biological processes. How to correctly and efficiently detect protein interaction is... Protein–protein interactions (PPIs) play an important role in most of the biological processes. How to correctly and efficiently detect protein interaction is... |
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SubjectTerms | Algorithms Computational Biology - methods Helicobacter pylori Neural Networks (Computer) Protein Binding Protein Interaction Mapping - methods Support Vector Machine |
Title | Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network |
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