Predicting Drug-Target Interactions Via Dual-Stream Graph Neural Network

Li, Yuhui; Liang, Wei; Peng, Li; Zhang, Dafang; Yang, Cheng; Li, Kuan‐Ching

doi:10.1109/tcbb.2022.3204188

Cited by 25 publications

(10 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To solve complex tasks, learning models increasingly go deep (Cai et al, 2021 ; Li et al, 2022 ). Non-neural network style-based deep models demonstrate powerful learning abilities when they can go deep.…”

Section: Methodsmentioning

confidence: 99%

“…In particular, deep learning has obtained wide application in DTI prediction. For example, Zong et al ( 2017 ) developed a deep learning model based on the topology of a multipartite DTI network, Wang et al ( 2018 ) used a deep ensemble learning model with a stacked autoencoder, Öztürk et al ( 2018 ) designed a deep learning model with character representations, You et al ( 2019 ) exploited a deep ensemble learning method with LASSO regression, Cheng et al ( 2021 ) combined multi-head self-attention and graph attention network, Lee and Nam ( 2022 ) explored a sequence-based approach, Li et al ( 2022 ) designed a dual-stream graph neural network, Mukherjee et al ( 2022 ) used a deep graph convolutional network and LSTM, Zhang et al ( 2022b ) exploited a graph neural network, and Tayebi et al ( 2022 ) designed a deep ensemble-balanced learning model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identifying potential drug-target interactions based on ensemble deep learning

Zhou

Wang

Peng

et al. 2023

Front. Aging Neurosci.

View full text Add to dashboard Cite

IntroductionDrug-target interaction prediction is one important step in drug research and development. Experimental methods are time consuming and laborious.MethodsIn this study, we developed a novel DTI prediction method called EnGDD by combining initial feature acquisition, dimensional reduction, and DTI classification based on Gradient boosting neural network, Deep neural network, and Deep Forest.ResultsEnGDD was compared with seven stat-of-the-art DTI prediction methods (BLM-NII, NRLMF, WNNGIP, NEDTP, DTi2Vec, RoFDT, and MolTrans) on the nuclear receptor, GPCR, ion channel, and enzyme datasets under cross validations on drugs, targets, and drug-target pairs, respectively. EnGDD computed the best recall, accuracy, F1-score, AUC, and AUPR under the majority of conditions, demonstrating its powerful DTI identification performance. EnGDD predicted that D00182 and hsa2099, D07871 and hsa1813, DB00599 and hsa2562, D00002 and hsa10935 have a higher interaction probabilities among unknown drug-target pairs and may be potential DTIs on the four datasets, respectively. In particular, D00002 (Nadide) was identified to interact with hsa10935 (Mitochondrial peroxiredoxin3) whose up-regulation might be used to treat neurodegenerative diseases. Finally, EnGDD was used to find possible drug targets for Parkinson's disease and Alzheimer's disease after confirming its DTI identification performance. The results show that D01277, D04641, and D08969 may be applied to the treatment of Parkinson's disease through targeting hsa1813 (dopamine receptor D2) and D02173, D02558, and D03822 may be the clues of treatment for patients with Alzheimer's disease through targeting hsa5743 (prostaglandinendoperoxide synthase 2). The above prediction results need further biomedical validation.DiscussionWe anticipate that our proposed EnGDD model can help discover potential therapeutic clues for various diseases including neurodegenerative diseases.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying potential drug-target interactions based on ensemble deep learning

Zhou

Wang

Peng

et al. 2023

Front. Aging Neurosci.

View full text Add to dashboard Cite

show abstract

“…DT makes full use of the physical model, sensor update, and other functions to complete the mapping of real physical systems in virtual space so as to reflect the whole life cycle process of corresponding physical equipment. DT systems are widely used in various digital systems 13–15 and other scenarios, for example, combining DT technology with blockchain technology. DT technology can build a DT of a car and record all relevant information about the car, while blockchain technology can solve the problem of information security in the used car market 16–18 .…”

Section: Related Workmentioning

confidence: 99%

An evolutive framework for server placement optimization to digital twin networks

Xiao

Han

Weng

et al. 2023

Int J Communication

Self Cite

View full text Add to dashboard Cite

SummaryDigital twin network (DTN) is a foremost enabler for efficient optimization in modern networks, as it owns massive real‐time data and requires interaction with the physical network in real‐time. When constructing a DTN, it is necessary to deploy many servers in the physical network for digital models' storage, calculation, and communication. Evolutionary algorithms show outstanding global optimization capabilities compared to the constructive heuristic method in such an optimization problem. However, due to the high dimensionality of the problem and the complicated evaluation of the deployment plan, evolutionary algorithms easily fall into the optimum local at a high computational cost, given that the server placement problem is an NP‐hard combinatorial optimization problem. In this research, we propose an evolutionary framework for server layout optimization that significantly improves the optimization efficiency of evolutionary algorithms and reduces the algorithm's computational cost. An offline‐learning‐based approach is used to reduce the search space, and a self‐examining guided local search method is proposed to improve the search efficiency. Additionally, a look‐up table‐based hybrid approach is used for solution evaluation, reducing computational overhead. Experimental results show that the proposed framework and optimization strategy can significantly improve the evolutionary algorithm search efficiency and achieve excellent convergence performance.

show abstract

“…Knowledge graphs were employed for DDI detection in a study by Li et al (2021), according to [35]. Drug targets, drug similarities, and drug interactions all went into the making of this knowledge graph.…”

Section: Gcn-based Ddi Detection Using Knowledge Graphmentioning

confidence: 99%

Evaluating The Effectiveness Of Graph Convolutional Network For Detection Of Polypharmacy Side Effects

IBRAHIM,

Mohammed,

DARA

2023

Preprint

View full text Add to dashboard Cite

Polypharmacy is frequently used to treat numerous illnesses; however, it might have unintended consequences. Recent research using graph convolutional networks (GCNs) has demonstrated promising results in the difficult problem of polypharmacy side effect detection. Using accuracy (ACC), area under the receiver operating characteristic curve (AUC), and F1-score, this study presents a GCN-based model for detecting polypharmacy side effects. The model draws on pharmaceutical data from electronic health records to generate a medication network graph to learn the intricate interrelationships between medications and estimate the risk of adverse effects. The experimental results demonstrate that the proposed GCN-based model outperforms conventional machine learning techniques with an ACC of 91%, an AUC of 0.88, and an F1-score of 0.83. The findings of this article suggest that GCNs are a useful method for monitoring patients on several medications for adverse reactions with total accuracy of 98.47%. Additional study is required to refine the model and collect more data for use in actual clinical settings.

show abstract

Predicting Drug-Target Interactions Via Dual-Stream Graph Neural Network

Cited by 25 publications

References 68 publications

Identifying potential drug-target interactions based on ensemble deep learning

Identifying potential drug-target interactions based on ensemble deep learning

An evolutive framework for server placement optimization to digital twin networks

Evaluating The Effectiveness Of Graph Convolutional Network For Detection Of Polypharmacy Side Effects

Contact Info

Product

Resources

About