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An Improved Deep Forest Model for Predicting Self-Interacting Proteins From Protein Sequence Using Wavelet Transformation
Chen, ZH (Chen, Zhan-Heng)[ 1,2 ]; Li, LP (Li, Li-Ping)[ 1 ]; He, Z (He, Zhou)[ 3 ]; Zhou, JR (Zhou, Ji-Ren)[ 1 ]; Li, YM (Li, Yangming)[ 4 ]; Wong, L (Wong, Leon)[ 1,2 ]
2019
Source PublicationFRONTIERS IN GENETICS
ISSN1664-8021
Volume10Issue:3Pages:1-10
Abstract

Self-interacting proteins (SIPs), whose more than two identities can interact with each other, play significant roles in the understanding of cellular process and cell functions. Although a number of experimental methods have been designed to detect the SIPs, they remain to be extremely time-consuming, expensive, and challenging even nowadays. Therefore, there is an urgent need to develop the computational methods for predicting SIPs. In this study, we propose a deep forest based predictor for accurate prediction of SIPs using protein sequence information. More specifically, a novel feature representation method, which integrate position-specific scoring matrix (PSSM) with wavelet transform, is introduced. To evaluate the performance of the proposed method, cross-validation tests are performed on two widely used benchmark datasets. The experimental results show that the proposed model achieved high accuracies of 95.43 and 93.65% on human and yeast datasets, respectively. The AUC value for evaluating the performance of the proposed method was also reported. The AUC value for yeast and human datasets are 0.9203 and 0.9586, respectively. To further show the advantage of the proposed method, it is compared with several existing methods. The results demonstrate that the proposed model is better than other SIPs prediction methods. This work can offer an effective architecture to biologists in detecting new SIPs.

Keywordself-interacting proteins disease position-specific scoring matrix deep learning wavelet transform
DOI10.3389/fgene.2019.00090
Indexed BySCI
WOS IDWOS:000460013100001
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/5687
Collection多语种信息技术研究室
Affiliation1.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Univ Colorado, Coll Engn & Appl Sci, Boulder, CO 80309 USA
4.Rochester Inst Technol, ECTET, Rochester, NY 14623 USA
Recommended Citation
GB/T 7714
Chen, ZH ,Li, LP ,He, Z ,et al. An Improved Deep Forest Model for Predicting Self-Interacting Proteins From Protein Sequence Using Wavelet Transformation[J]. FRONTIERS IN GENETICS,2019,10(3):1-10.
APA Chen, ZH ,Li, LP ,He, Z ,Zhou, JR ,Li, YM ,&Wong, L .(2019).An Improved Deep Forest Model for Predicting Self-Interacting Proteins From Protein Sequence Using Wavelet Transformation.FRONTIERS IN GENETICS,10(3),1-10.
MLA Chen, ZH ,et al."An Improved Deep Forest Model for Predicting Self-Interacting Proteins From Protein Sequence Using Wavelet Transformation".FRONTIERS IN GENETICS 10.3(2019):1-10.
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