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

IF 3.743 Q2 Biochemistry, Genetics and Molecular Biology
Yan-Bin Wang, Zhu-Hong You, Xiao Li, Tong-Hai Jiang, Xing Chen, Xi Zhou and Lei Wang
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引用次数: 104

摘要

蛋白质-蛋白质相互作用(PPIs)在大多数生物过程中起着重要作用。如何正确有效地检测蛋白质相互作用是一个值得研究的问题。尽管高通量技术提供了检测大规模ppi的可能性,但这些技术不能用于检测整个ppi,并且可能产生不可靠的数据。为了解决这一问题,本研究提出了一种新的计算方法,利用蛋白质序列的信息有效地预测PPIs。该方法采用Zernike矩从位置特定评分矩阵(PSSM)中提取蛋白质序列特征。然后,使用堆叠自编码器对提取的特征进行重构。最后,利用一种新的概率分类向量机(PCVM)分类器对蛋白质相互作用进行预测。在酵母和幽门螺杆菌的PPIs数据集上,该方法的平均准确率分别为96.60%和91.19%。结果表明,该方法比其他检测方法具有更好的检测PPIs的能力。该方法也应用于其他物种的PPIs预测,取得了令人满意的结果。为了评估我们的方法的能力,我们将其与酵母数据集的最先进的支持向量机(SVM)分类器进行了比较。多次实验结果证明了该方法的有效性和可行性,为蛋白质组学研究做出了重要贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predicting protein–protein interactions from protein sequences by a stacked sparse autoencoder deep neural network

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.

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来源期刊
Molecular BioSystems
Molecular BioSystems 生物-生化与分子生物学
CiteScore
2.94
自引率
0.00%
发文量
0
审稿时长
2.6 months
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.
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