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Improving Prediction of Self-interacting Proteins Using Stacked Sparse Auto-Encoder with PSSM profiles
Wang, YB (Wang, Yan-Bin); You, ZH (You, Zhu-Hong); Li, LP (Li, Li-Ping); Huang, DS (Huang, De-Shuang); Zhou, FF (Zhou, Feng-Feng); Yang, S (Yang, Shan); You, ZH; Li, LP
2018
发表期刊INTERNATIONAL JOURNAL OF BIOLOGICAL SCIENCES
卷号14期号:8页码:983-991
摘要

Self-interacting proteins (SIPs) play a significant role in the execution of most important molecular processes in cells, such as signal transduction, gene expression regulation, immune response and enzyme activation. Although the traditional experimental methods can be used to generate SIPs data, it is very expensive and time-consuming based only on biological technique. Therefore, it is important and urgent to develop an efficient computational method for SIPs detection. In this study, we present a novel SIPs identification method based on machine learning technology by combing the Zernike Moments (ZMs) descriptor on Position Specific Scoring Matrix (PSSM) with Probabilistic Classification Vector Machines (PCVM) and Stacked Sparse Auto-Encoder (SSAE). More specifically, an efficient feature extraction technique called ZMs is firstly utilized to generate feature vectors on Position Specific Scoring Matrix (PSSM); Then, Deep neural network is employed for reducing the feature dimensions and noise; Finally, the Probabilistic Classification Vector Machine is used to execute the classification. The prediction performance of the proposed method is evaluated on S.erevisiae and Human SIPs datasets via cross-validation. The experimental results indicate that the proposed method can achieve good accuracies of 92.55% and 97.47%, respectively. To further evaluate the advantage of our scheme for SIPs prediction, we also compared the PCVM classifier with the Support Vector Machine (SVM) and other existing techniques on the same data sets. Comparison results reveal that the proposed strategy is outperforms other methods and could be a used tool for identifying SIPs.

关键词Deep Learning Zernike Moments Probabilistic Classification Vector Machines
DOI10.7150/ijbs.23817
收录类别SCI
WOS记录号WOS:000433262600020
引用统计
文献类型期刊论文
条目标识符http://ir.xjipc.cas.cn/handle/365002/5480
专题多语种信息技术研究室
通讯作者You, ZH; Li, LP
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
3.Tongji Univ, Inst Machine Learning & Syst Biol, Sch Elect & Informat Engn, Caoan Rd 4800, Shanghai 201804, Peoples R China
4.Jilin Univ, Coll Comp Sci & Technol, Minist Educ, Changchun 130012, Jilin, Peoples R China
5.Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Jilin, Peoples R China
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GB/T 7714
Wang, YB ,You, ZH ,Li, LP ,et al. Improving Prediction of Self-interacting Proteins Using Stacked Sparse Auto-Encoder with PSSM profiles[J]. INTERNATIONAL JOURNAL OF BIOLOGICAL SCIENCES,2018,14(8):983-991.
APA Wang, YB .,You, ZH .,Li, LP .,Huang, DS .,Zhou, FF .,...&Li, LP.(2018).Improving Prediction of Self-interacting Proteins Using Stacked Sparse Auto-Encoder with PSSM profiles.INTERNATIONAL JOURNAL OF BIOLOGICAL SCIENCES,14(8),983-991.
MLA Wang, YB ,et al."Improving Prediction of Self-interacting Proteins Using Stacked Sparse Auto-Encoder with PSSM profiles".INTERNATIONAL JOURNAL OF BIOLOGICAL SCIENCES 14.8(2018):983-991.
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