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A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network
Wang, L (Wang, Lei); You, ZH (You, Zhu-Hong); Chen, X (Chen, Xing); Xia, SX (Xia, Shi-Xiong); Liu, F (Liu, Feng); Yan, X (Yan, Xin); Zhou, Y (Zhou, Yong); Song, KJ (Song, Ke-Jian)
2018
Source PublicationJOURNAL OF COMPUTATIONAL BIOLOGY
ISSN1066-5277
Volume25Issue:3Pages:361-373
Abstract

Identifying the interaction between drugs and target proteins is an important area of drug research, which provides a broad prospect for low-risk and faster drug development. However, due to the limitations of traditional experiments when revealing drug-protein interactions (DTIs), the screening of targets not only takes a lot of time and money but also has high false-positive and false-negative rates. Therefore, it is imperative to develop effective automatic computational methods to accurately predict DTIs in the postgenome era. In this article, we propose a new computational method for predicting DTIs from drug molecular structure and protein sequence by using the stacked autoencoder of deep learning, which can adequately extract the raw data information. The proposed method has the advantage that it can automatically mine the hidden information from protein sequences and generate highly representative features through iterations of multiple layers. The feature descriptors are then constructed by combining the molecular substructure fingerprint information, and fed into the rotation forest for accurate prediction. The experimental results of fivefold cross-validation indicate that the proposed method achieves superior performance on gold standard data sets (enzymes,ion channels,GPCRs[G-protein-coupled receptors], and nuclear receptors) with accuracy of 0.9414, 0.9116, 0.8669, and 0.8056, respectively. We further comprehensively explore the performance of the proposed method by comparing it with other feature extraction algorithms, state-of-the-art classifiers, and other excellent methods on the same data set. The excellent comparison results demonstrate that the proposed method is highly competitive when predicting drug-target interactions.

KeywordDeep Learning Drug-target Interactions Position-specific Scoring Matrix Stacked Autoencoder
DOI10.1089/cmb.2017.0135
Indexed BySCI
WOS IDWOS:000429742800010
Citation statistics
Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/5288
Collection多语种信息技术研究室
Corresponding AuthorYou, ZH (You, Zhu-Hong)
Affiliation1.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou, Peoples R China
2.Zaozhuang Univ, Coll Informat Sci & Engn, Zaozhuang, Peoples R China
3.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, China Beijing South Rd,40-1, Urumqi 830011, Peoples R China
4.China Univ Min & Technol, Sch Informat & Control Engn, 1 Univ Rd, Xuzhou 221116, Peoples R China
5.China Natl Coal Assoc, Beijing, Peoples R China
6.Zaozhuang Univ, Sch Foreign Languages, Zaozhuang, Peoples R China
7.JiangXi Univ Sci & Technol, Sch Informat Engn, Ganzhou, Peoples R China
Recommended Citation
GB/T 7714
Wang, L ,You, ZH ,Chen, X ,et al. A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network[J]. JOURNAL OF COMPUTATIONAL BIOLOGY,2018,25(3):361-373.
APA Wang, L .,You, ZH .,Chen, X .,Xia, SX .,Liu, F .,...&Song, KJ .(2018).A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network.JOURNAL OF COMPUTATIONAL BIOLOGY,25(3),361-373.
MLA Wang, L ,et al."A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network".JOURNAL OF COMPUTATIONAL BIOLOGY 25.3(2018):361-373.
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