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) | |
刊名 | INTERNATIONAL JOURNAL OF BIOLOGICAL SCIENCES |
2018 | |
卷号 | 14期号:8页码:983-991 |
关键词 | Deep Learning Zernike Moments Probabilistic Classification Vector Machines |
ISSN号 | 1449-2288 |
DOI | 10.7150/ijbs.23817 |
英文摘要 | 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. |
WOS记录号 | WOS:000433262600020 |
内容类型 | 期刊论文 |
源URL | [http://ir.xjipc.cas.cn/handle/365002/5480] |
专题 | 新疆理化技术研究所_多语种信息技术研究室 |
通讯作者 | You, ZH (You, Zhu-Hong) |
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 |
推荐引用方式 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 ,&Yang, S .(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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