Yan-Bin Wang, Zhu-Hong You, Xiao Li, Tong-Hai Jiang, Xing Chen, Xi Zhou and Lei Wang
{"title":"基于堆叠稀疏自编码器深度神经网络的蛋白质序列相互作用预测","authors":"Yan-Bin Wang, Zhu-Hong You, Xiao Li, Tong-Hai Jiang, Xing Chen, Xi Zhou and Lei Wang","doi":"10.1039/C7MB00188F","DOIUrl":null,"url":null,"abstract":"<p >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 <em>Yeast</em> and <em>H. pylori</em>, 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 <em>Yeast</em> dataset. The results obtained <em>via</em> multiple experiments prove that our method is powerful, efficient, feasible, and make a great contribution to proteomics research.</p>","PeriodicalId":90,"journal":{"name":"Molecular BioSystems","volume":" 7","pages":" 1336-1344"},"PeriodicalIF":3.7430,"publicationDate":"2017-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1039/C7MB00188F","citationCount":"104","resultStr":"{\"title\":\"Predicting protein–protein interactions from protein sequences by a stacked sparse autoencoder deep neural network\",\"authors\":\"Yan-Bin Wang, Zhu-Hong You, Xiao Li, Tong-Hai Jiang, Xing Chen, Xi Zhou and Lei Wang\",\"doi\":\"10.1039/C7MB00188F\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >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 <em>Yeast</em> and <em>H. pylori</em>, 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 <em>Yeast</em> dataset. The results obtained <em>via</em> multiple experiments prove that our method is powerful, efficient, feasible, and make a great contribution to proteomics research.</p>\",\"PeriodicalId\":90,\"journal\":{\"name\":\"Molecular BioSystems\",\"volume\":\" 7\",\"pages\":\" 1336-1344\"},\"PeriodicalIF\":3.7430,\"publicationDate\":\"2017-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1039/C7MB00188F\",\"citationCount\":\"104\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular BioSystems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://pubs.rsc.org/en/content/articlelanding/2017/mb/c7mb00188f\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Biochemistry, Genetics and Molecular Biology\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular BioSystems","FirstCategoryId":"1085","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2017/mb/c7mb00188f","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
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 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.
期刊介绍:
Molecular Omics publishes molecular level experimental and bioinformatics research in the -omics sciences, including genomics, proteomics, transcriptomics and metabolomics. We will also welcome multidisciplinary papers presenting studies combining different types of omics, or the interface of omics and other fields such as systems biology or chemical biology.