共 81 条
DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network
被引:56
作者:
Chen, Cheng
[1
,2
,3
]
Shi, Han
[4
]
Jiang, Zhiwen
[1
,2
]
Salhi, Adil
[5
]
Chen, Ruixin
[1
,2
]
Cui, Xuefeng
[3
]
Yu, Bin
[1
,2
,6
]
机构:
[1] Qingdao Univ Sci & Technol, Coll Math & Phys, Qingdao 266061, Peoples R China
[2] Qingdao Univ Sci & Technol, Artificial Intelligence & Biomed Big Data Res Ctr, Qingdao 266061, Peoples R China
[3] Shandong Univ, Sch Comp Sci & Technol, Qingdao 266237, Peoples R China
[4] Chinese Acad Sci, CAS Ctr Excellence Mol Plant Sci, Key Lab Synthet Biol, Shanghai 200032, Peoples R China
[5] King Abdullah Univ Sci & Technol KAUST, Computat Biosci Res Ctr CBRC, Thuwal 23955, Saudi Arabia
[6] Key Lab Computat Sci & Applicat Hainan Prov, Haikou 571158, Hainan, Peoples R China
基金:
中国国家自然科学基金;
关键词:
Drug-target interactions;
Multi-information fusion;
XGBoost;
Deep neural network;
INFORMATION;
INTEGRATION;
D O I:
10.1016/j.compbiomed.2021.104676
中图分类号:
Q [生物科学];
学科分类号:
07 ;
0710 ;
09 ;
摘要:
Analysis and prediction of drug-target interactions (DTIs) play an important role in understanding drug mechanisms, as well as drug repositioning and design. Machine learning (ML)-based methods for DTIs prediction can mitigate the shortcomings of time-consuming and labor-intensive experimental approaches, while providing new ideas and insights for drug design. We propose a novel pipeline for predicting drug-target interactions, called DNN-DTIs. First, the target information is characterized by a number of features, namely, pseudo-amino acid composition, pseudo position-specific scoring matrix, conjoint triad composition, transition and distribution, Moreau-Broto autocorrelation, and structural features. The drug compounds are subsequently encoded using substructure fingerprints. Next, eXtreme gradient boosting (XGBoost) is used to determine the subset of non redundant features of importance. The optimal balanced set of sample vectors is obtained by applying the synthetic minority oversampling technique (SMOTE). Finally, a DTIs predictor, DNN-DTIs, is developed based on a deep neural network (DNN) via a layer-by-layer learning scheme. Experimental results indicate that DNN-DTIs achieves better performance than other state-of-the-art predictors with ACC values of 98.78%, 98.60%, 97.98%, 98.24% and 98.00% on Enzyme, Ion Channels (IC), GPCR, Nuclear Receptors (NR) and Kuang's datasets. Therefore, the accurate prediction performance of DNN-DTIs makes it a favored choice for contributing to the study of DTIs, especially drug repositioning.
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页数:14
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