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Lin J, Hong B, Cai Z, Lu P, Lin K. MASMDDI: multi-layer adaptive soft-mask graph neural network for drug-drug interaction prediction. Front Pharmacol 2024; 15:1369403. [PMID: 38831885 PMCID: PMC11144894 DOI: 10.3389/fphar.2024.1369403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 04/23/2024] [Indexed: 06/05/2024] Open
Abstract
Accurately predicting Drug-Drug Interaction (DDI) is a critical and challenging aspect of the drug discovery process, particularly in preventing adverse reactions in patients undergoing combination therapy. However, current DDI prediction methods often overlook the interaction information between chemical substructures of drugs, focusing solely on the interaction information between drugs and failing to capture sufficient chemical substructure details. To address this limitation, we introduce a novel DDI prediction method: Multi-layer Adaptive Soft Mask Graph Neural Network (MASMDDI). Specifically, we first design a multi-layer adaptive soft mask graph neural network to extract substructures from molecular graphs. Second, we employ an attention mechanism to mine substructure feature information and update latent features. In this process, to optimize the final feature representation, we decompose drug-drug interactions into pairwise interaction correlations between the core substructures of each drug. Third, we use these features to predict the interaction probabilities of DDI tuples and evaluate the model using real-world datasets. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods in DDI prediction. Furthermore, MASMDDI exhibits excellent performance in predicting DDIs of unknown drugs in two tasks that are more aligned with real-world scenarios. In particular, in the transductive scenario using the DrugBank dataset, the ACC and AUROC and AUPRC scores of MASMDDI are 0.9596, 0.9903, and 0.9894, which are 2% higher than the best performing baseline.
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Affiliation(s)
- Junpeng Lin
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Binsheng Hong
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Zhongqi Cai
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Ping Lu
- School of Economics and Management, Xiamen University of Technology, Xiamen, China
| | - Kaibiao Lin
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
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2
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Zhang Y, Deng Z, Xu X, Feng Y, Junliang S. Application of Artificial Intelligence in Drug-Drug Interactions Prediction: A Review. J Chem Inf Model 2024; 64:2158-2173. [PMID: 37458400 DOI: 10.1021/acs.jcim.3c00582] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Drug-drug interactions (DDI) are a critical aspect of drug research that can have adverse effects on patients and can lead to serious consequences. Predicting these events accurately can significantly improve clinicians' ability to make better decisions and establish optimal treatment regimens. However, manually detecting these interactions is time-consuming and labor-intensive. Utilizing the advancements in Artificial Intelligence (AI) is essential for achieving accurate forecasts of DDIs. In this review, DDI prediction tasks are classified into three types according to the type of DDI prediction: undirected DDI prediction, DDI events prediction, and Asymmetric DDI prediction. The paper then reviews the progress of AI for each of these three prediction tasks in DDI and provides a summary of the data sets used as well as the representative methods used in these three prediction directions. In this review, we aim to provide a comprehensive overview of drug interaction prediction. The first section introduces commonly used databases and presents an overview of current research advancements and techniques across three domains of DDI. Additionally, we introduce classical machine learning techniques for predicting undirected drug interactions and provide a timeline for the progression of the predicted drug interaction events. At last, we debate the difficulties and prospects of AI approaches at predicting DDI, emphasizing their potential for improving clinical decision-making and patient outcomes.
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Affiliation(s)
- Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Zengqian Deng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Xiaoyu Xu
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Yinfei Feng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Shang Junliang
- School of Information Science and Engineering, Qufu Normal University, Rizhao, 276800, China
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3
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Yan X, Gu C, Feng Y, Han J. Predicting Drug-drug Interaction with Graph Mutual Interaction Attention Mechanism. Methods 2024; 223:16-25. [PMID: 38262485 DOI: 10.1016/j.ymeth.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/04/2024] [Accepted: 01/19/2024] [Indexed: 01/25/2024] Open
Abstract
Effective representation of molecules is a crucial step in AI-driven drug design and drug discovery, especially for drug-drug interaction (DDIs) prediction. Previous work usually models the drug information from the drug-related knowledge graph or the single drug molecules, but the interaction information between molecular substructures of drug pair is seldom considered, thus often ignoring the influence of bond information on atom node representation, leading to insufficient drug representation. Moreover, key molecular substructures have significant contribution to the DDIs prediction results. Therefore, in this work, we propose a novel Graph learning framework of Mutual Interaction Attention mechanism (called GMIA) to predict DDIs by effectively representing the drug molecules. Specifically, we build the node-edge message communication encoder to aggregate atom node and the incoming edge information for atom node representation and design the mutual interaction attention decoder to capture the mutual interaction context between molecular graphs of drug pairs. GMIA can bridge the gap between two encoders for the single drug molecules by attention mechanism. We also design a co-attention matrix to analyze the significance of different-size substructures obtained from the encoder-decoder layer and provide interpretability. In comparison with other recent state-of-the-art methods, our GMIA achieves the best results in terms of area under the precision-recall-curve (AUPR), area under the ROC curve (AUC), and F1 score on two different scale datasets. The case study indicates that our GMIA can detect the key substructure for potential DDIs, demonstrating the enhanced performance and interpretation ability of GMIA.
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Affiliation(s)
- Xiaoying Yan
- College of Computer Science, Xi'an Shiyou University, Xi'an 710065, China.
| | - Chi Gu
- College of Computer Science, Xi'an Shiyou University, Xi'an 710065, China
| | - Yuehua Feng
- College of Computer Science, Xi'an Shiyou University, Xi'an 710065, China
| | - Jiaxin Han
- College of Computer Science, Xi'an Shiyou University, Xi'an 710065, China
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4
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Deng Z, Xu J, Feng Y, Dong L, Zhang Y. MAVGAE: a multimodal framework for predicting asymmetric drug-drug interactions based on variational graph autoencoder. Comput Methods Biomech Biomed Engin 2024:1-13. [PMID: 38314513 DOI: 10.1080/10255842.2024.2311315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 01/21/2024] [Indexed: 02/06/2024]
Abstract
Drug-drug interactions refer to the phenomena wherein the potency, duration, or effectiveness of one or multiple drugs undergo alterations of varying degrees as a result of their concurrent or sequential usage. The accurate identification of potential drug interactions plays a pivotal role in mitigating the risks associated with drug administration in patients, it also helps in minimizing the likelihood of hazardous situations arising during a patient's course of treatment. However, researchers have found that there is a problem of asymmetric drug interactions, where one drug may affect another but not vice versa. This adds to the difficulty of prediction, so in polypharmacy, the order of drug administration is critical to efficacy and safety, and few current studies predict asymmetric DDIs. Aiming at the above problems, we propose a framework based on multimodal data and a variational graph autoencoder named MAVGAE for predicting asymmetric drug interactions. The framework initially encodes multimodal data into low-dimensional representations and then utilizes a variational graph autoencoder for encoding and decoding. During the model training process, supervised learning is employed for the classification task with the incorporation of heterogeneity information, ensuring accurate prediction of drug interactions. Experimental validation on a large-scale drug dataset demonstrates the framework's high accuracy and reliability in predicting non-symmetrical drug interactions, offering effective support and guidance for drug research.
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Affiliation(s)
- Zengqian Deng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
| | - Jie Xu
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Yinfei Feng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
| | - Liangcheng Dong
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
| | - Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
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5
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Zhang R, Wang X, Wang P, Meng Z, Cui W, Zhou Y. HTCL-DDI: a hierarchical triple-view contrastive learning framework for drug-drug interaction prediction. Brief Bioinform 2023; 24:bbad324. [PMID: 37742052 DOI: 10.1093/bib/bbad324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/26/2023] [Accepted: 08/24/2023] [Indexed: 09/25/2023] Open
Abstract
Drug-drug interaction (DDI) prediction can discover potential risks of drug combinations in advance by detecting drug pairs that are likely to interact with each other, sparking an increasing demand for computational methods of DDI prediction. However, existing computational DDI methods mostly rely on the single-view paradigm, failing to handle the complex features and intricate patterns of DDIs due to the limited expressiveness of the single view. To this end, we propose a Hierarchical Triple-view Contrastive Learning framework for Drug-Drug Interaction prediction (HTCL-DDI), leveraging the molecular, structural and semantic views to model the complicated information involved in DDI prediction. To aggregate the intra-molecular compositional and structural information, we present a dual attention-aware network in the molecular view. Based on the molecular view, to further capture inter-molecular information, we utilize the one-hop neighboring information and high-order semantic relations in the structural view and semantic view, respectively. Then, we introduce contrastive learning to enhance drug representation learning from multifaceted aspects and improve the robustness of HTCL-DDI. Finally, we conduct extensive experiments on three real-world datasets. All the experimental results show the significant improvement of HTCL-DDI over the state-of-the-art methods, which also demonstrates that HTCL-DDI opens new avenues for ensuring medication safety and identifying synergistic drug combinations.
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Affiliation(s)
- Ran Zhang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xuezhi Wang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Pengfei Wang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhen Meng
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Wenjuan Cui
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuanchun Zhou
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
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6
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Zhang M, Gao H, Liao X, Ning B, Gu H, Yu B. DBGRU-SE: predicting drug-drug interactions based on double BiGRU and squeeze-and-excitation attention mechanism. Brief Bioinform 2023:7176312. [PMID: 37225428 DOI: 10.1093/bib/bbad184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 04/03/2023] [Accepted: 04/23/2023] [Indexed: 05/26/2023] Open
Abstract
The prediction of drug-drug interactions (DDIs) is essential for the development and repositioning of new drugs. Meanwhile, they play a vital role in the fields of biopharmaceuticals, disease diagnosis and pharmacological treatment. This article proposes a new method called DBGRU-SE for predicting DDIs. Firstly, FP3 fingerprints, MACCS fingerprints, Pubchem fingerprints and 1D and 2D molecular descriptors are used to extract the feature information of the drugs. Secondly, Group Lasso is used to remove redundant features. Then, SMOTE-ENN is applied to balance the data to obtain the best feature vectors. Finally, the best feature vectors are fed into the classifier combining BiGRU and squeeze-and-excitation (SE) attention mechanisms to predict DDIs. After applying five-fold cross-validation, The ACC values of DBGRU-SE model on the two datasets are 97.51 and 94.98%, and the AUC are 99.60 and 98.85%, respectively. The results showed that DBGRU-SE had good predictive performance for drug-drug interactions.
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Affiliation(s)
| | - Hongli Gao
- Qingdao University of Science and Technology, China
| | - Xin Liao
- Qingdao University of Science and Technology, China
| | - Baoxing Ning
- Qingdao University of Science and Technology, China
| | - Haiming Gu
- Qingdao University of Science and Technology, China
| | - Bin Yu
- Qingdao University of Science and Technology, China
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7
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Feng YH, Zhang SW, Feng YY, Zhang QQ, Shi MH, Shi JY. A social theory-enhanced graph representation learning framework for multitask prediction of drug-drug interactions. Brief Bioinform 2023; 24:6987818. [PMID: 36642408 DOI: 10.1093/bib/bbac602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 11/30/2022] [Accepted: 12/06/2022] [Indexed: 01/17/2023] Open
Abstract
Current machine learning-based methods have achieved inspiring predictions in the scenarios of mono-type and multi-type drug-drug interactions (DDIs), but they all ignore enhancive and depressive pharmacological changes triggered by DDIs. In addition, these pharmacological changes are asymmetric since the roles of two drugs in an interaction are different. More importantly, these pharmacological changes imply significant topological patterns among DDIs. To address the above issues, we first leverage Balance theory and Status theory in social networks to reveal the topological patterns among directed pharmacological DDIs, which are modeled as a signed and directed network. Then, we design a novel graph representation learning model named SGRL-DDI (social theory-enhanced graph representation learning for DDI) to realize the multitask prediction of DDIs. SGRL-DDI model can capture the task-joint information by integrating relation graph convolutional networks with Balance and Status patterns. Moreover, we utilize task-specific deep neural networks to perform two tasks, including the prediction of enhancive/depressive DDIs and the prediction of directed DDIs. Based on DDI entries collected from DrugBank, the superiority of our model is demonstrated by the comparison with other state-of-the-art methods. Furthermore, the ablation study verifies that Balance and Status patterns help characterize directed pharmacological DDIs, and that the joint of two tasks provides better DDI representations than individual tasks. Last, we demonstrate the practical effectiveness of our model by a version-dependent test, where 88.47 and 81.38% DDI out of newly added entries provided by the latest release of DrugBank are validated in two predicting tasks respectively.
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Affiliation(s)
- Yue-Hua Feng
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yi-Yang Feng
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Qing-Qing Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Ming-Hui Shi
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
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8
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Pan D, Quan L, Jin Z, Chen T, Wang X, Xie J, Wu T, Lyu Q. Multisource Attention-Mechanism-Based Encoder-Decoder Model for Predicting Drug-Drug Interaction Events. J Chem Inf Model 2022; 62:6258-6270. [PMID: 36449561 DOI: 10.1021/acs.jcim.2c01112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Many computational methods have been proposed to predict drug-drug interactions (DDIs), which can occur when combining drugs to treat various diseases, but most mainly utilize single-source features of drugs, which is inadequate for drug representation. To fill this gap, we propose two attention-mechanism-based encoder-decoder models that incorporate multisource information: one is MAEDDI, which can predict DDIs, and the other is MAEDDIE, which can make further DDI-associated event predictions for drug pairs with DDIs. To better express the drug feature, we used three encoding methods to encode the drugs, integrating the self-attention mechanism, cross-attention mechanism, and graph attention network to construct a multisource feature fusion network. Experiments showed that both MAEDDI and MAEDDIE performed better than some state-of-the-art methods in various validation attempts at different experimental tasks. The visualization analysis showed that the semantic features of drug pairs learned from our models had a good drug representation. In practice, MAEDDIE successfully screened 43 DDI events on favipiravir, an influenza antiviral drug, with a success rate of nearly 50%. Our model achieved competitive results, mainly owing to the design of sequence-based, structural, biochemical, and statistical multisource features. Moreover, different encoders constructed based on different features learn the interrelationship information between drug pairs, and the different representations of these drug pairs are incorporated to predict the target problem. All of these encoders were designed to better characterize the complex DDI relationships, allowing us to achieve high generalization in DDI and DDI-associated event predations.
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Affiliation(s)
- Deng Pan
- School of Computer Science and Technology, Soochow University, Suzhou215006, China
| | - Lijun Quan
- School of Computer Science and Technology, Soochow University, Suzhou215006, China.,Province Key Lab for Information Processing Technologies, Soochow University, Suzhou215006, China.,Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing210000, China
| | - Zhi Jin
- School of Computer Science and Technology, Soochow University, Suzhou215006, China
| | - Taoning Chen
- School of Computer Science and Technology, Soochow University, Suzhou215006, China
| | - Xuejiao Wang
- School of Computer Science and Technology, Soochow University, Suzhou215006, China
| | - Jingxin Xie
- School of Computer Science and Technology, Soochow University, Suzhou215006, China
| | - Tingfang Wu
- School of Computer Science and Technology, Soochow University, Suzhou215006, China.,Province Key Lab for Information Processing Technologies, Soochow University, Suzhou215006, China.,Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing210000, China
| | - Qiang Lyu
- School of Computer Science and Technology, Soochow University, Suzhou215006, China.,Province Key Lab for Information Processing Technologies, Soochow University, Suzhou215006, China.,Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing210000, China
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9
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Shtar G, Greenstein-Messica A, Mazuz E, Rokach L, Shapira B. Predicting drug characteristics using biomedical text embedding. BMC Bioinformatics 2022; 23:526. [PMID: 36476573 PMCID: PMC9730627 DOI: 10.1186/s12859-022-05083-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 11/25/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Drug-drug interactions (DDIs) are preventable causes of medical injuries and often result in doctor and emergency room visits. Previous research demonstrates the effectiveness of using matrix completion approaches based on known drug interactions to predict unknown Drug-drug interactions. However, in the case of a new drug, where there is limited or no knowledge regarding the drug's existing interactions, such an approach is unsuitable, and other drug's preferences can be used to accurately predict new Drug-drug interactions. METHODS We propose adjacency biomedical text embedding (ABTE) to address this limitation by using a hybrid approach which combines known drugs' interactions and the drug's biomedical text embeddings to predict the DDIs of both new and well known drugs. RESULTS Our evaluation demonstrates the superiority of this approach compared to recently published DDI prediction models and matrix factorization-based approaches. Furthermore, we compared the use of different text embedding methods in ABTE, and found that the concept embedding approach, which involves biomedical information in the embedding process, provides the highest performance for this task. Additionally, we demonstrate the effectiveness of leveraging biomedical text embedding for additional drugs' biomedical prediction task by presenting text embedding's contribution to a multi-modal pregnancy drug safety classification. CONCLUSION Text and concept embeddings created by analyzing a domain-specific large-scale biomedical corpora can be used for predicting drug-related properties such as Drug-drug interactions and drug safety prediction. Prediction models based on the embeddings resulted in comparable results to hand-crafted features, however text embeddings do not require manual categorization or data collection and rely solely on the published literature.
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Affiliation(s)
- Guy Shtar
- grid.7489.20000 0004 1937 0511Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Asnat Greenstein-Messica
- grid.7489.20000 0004 1937 0511Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Eyal Mazuz
- grid.7489.20000 0004 1937 0511Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Lior Rokach
- grid.7489.20000 0004 1937 0511Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Bracha Shapira
- grid.7489.20000 0004 1937 0511Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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10
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A survey of graph neural networks in various learning paradigms: methods, applications, and challenges. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10321-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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11
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Al-Rabeah MH, Lakizadeh A. Prediction of drug-drug interaction events using graph neural networks based feature extraction. Sci Rep 2022; 12:15590. [PMID: 36114278 PMCID: PMC9481536 DOI: 10.1038/s41598-022-19999-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/07/2022] [Indexed: 11/12/2022] Open
Abstract
The prevalence of multi_drug therapies has been increasing in recent years, particularly among the elderly who are suffering from several diseases. However, unexpected Drug_Drug interaction (DDI) can cause adverse reactions or critical toxicity, which puts patients in danger. As the need for multi_drug treatment increases, it's becoming increasingly necessary to discover DDIs. Nevertheless, DDIs detection in an extensive number of drug pairs, both in-vitro and in-vivo, is costly and laborious. Therefore, DDI identification is one of the most concerns in drug-related researches. In this paper, we propose GNN-DDI, a deep learning-based method for predicting DDI-associated events in two stages. In the first stage, we collect the drugs information from different sources and then integrate them through the formation of an attributed heterogeneous network and generate a drug embedding vector based on different drug interaction types and drug attributes. In the second stage, we aggregate the representation vectors then predictions of the DDIs and their events are performed through a deep multi-model framework. Various evaluation results show that the proposed method can outperform state-of-the methods in the prediction of drug-drug interaction-associated events. The experimental results indicate that producing the drug's representations based on different drug interaction types and attributes is efficient and effective and can better show the intrinsic characteristics of a drug.
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12
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Kang LP, Lin KB, Lu P, Yang F, Chen JP. Multitype drug interaction prediction based on the deep fusion of drug features and topological relationships. PLoS One 2022; 17:e0273764. [PMID: 36037188 PMCID: PMC9423685 DOI: 10.1371/journal.pone.0273764] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/14/2022] [Indexed: 11/21/2022] Open
Abstract
Drug–drug interaction (DDI) prediction has received considerable attention from industry and academia. Most existing methods predict DDIs from drug attributes or relationships with neighbors, which does not guarantee that informative drug embeddings for prediction will be obtained. To address this limitation, we propose a multitype drug interaction prediction method based on the deep fusion of drug features and topological relationships, abbreviated DM-DDI. The proposed method adopts a deep fusion strategy to combine drug features and topologies to learn representative drug embeddings for DDI prediction. Specifically, a deep neural network model is first used on the drug feature matrix to extract feature information, while a graph convolutional network model is employed to capture structural information from the adjacency matrix. Then, we adopt delivery operations that allow the two models to exchange information between layers, as well as an attention mechanism for a weighted fusion of the two learned embeddings before the output layer. Finally, the unified drug embeddings for the downstream task are obtained. We conducted extensive experiments on real-world datasets, the experimental results demonstrated that DM-DDI achieved more accurate prediction results than state-of-the-art baselines. Furthermore, in two tasks that are more similar to real-world scenarios, DM-DDI outperformed other prediction methods for unknown drugs.
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Affiliation(s)
- Li-Ping Kang
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Kai-Biao Lin
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
- Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Provincial University, Putian, China
- * E-mail:
| | - Ping Lu
- School of Economics and Management, Xiamen University of Technology, Xiamen, China
| | - Fan Yang
- Department of Automation, Xiamen University, Xiamen, China
| | - Jin-Po Chen
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
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13
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Qiu Y, Zhang Y, Deng Y, Liu S, Zhang W. A Comprehensive Review of Computational Methods For Drug-Drug Interaction Detection. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1968-1985. [PMID: 34003753 DOI: 10.1109/tcbb.2021.3081268] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance, which provides effective and safe co-prescriptions of multiple drugs. Since laboratory researches are often complicated, costly and time-consuming, it's urgent to develop computational approaches to detect drug-drug interactions. In this paper, we conduct a comprehensive review of state-of-the-art computational methods falling into three categories: literature-based extraction methods, machine learning-based prediction methods and pharmacovigilance-based data mining methods. Literature-based extraction methods detect DDIs from published literature using natural language processing techniques; machine learning-based prediction methods build prediction models based on the known DDIs in databases and predict novel ones; pharmacovigilance-based data mining methods usually apply statistical techniques on various electronic data to detect drug-drug interaction signals. We first present the taxonomy of drug-drug interaction detection methods and provide the outlines of three categories of methods. Afterwards, we respectively introduce research backgrounds and data sources of three categories, and illustrate their representative approaches as well as evaluation metrics. Finally, we discuss the current challenges of existing methods and highlight potential opportunities for future directions.
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14
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Yu H, Zhao S, Shi J. STNN-DDI: a Substructure-aware Tensor Neural Network to predict Drug-Drug Interactions. Brief Bioinform 2022; 23:6603447. [PMID: 35667078 DOI: 10.1093/bib/bbac209] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/25/2022] [Accepted: 05/05/2022] [Indexed: 11/14/2022] Open
Abstract
Computational prediction of multiple-type drug-drug interaction (DDI) helps reduce unexpected side effects in poly-drug treatments. Although existing computational approaches achieve inspiring results, they ignore to study which local structures of drugs cause DDIs, and their interpretability is still weak. In this paper, by supposing that the interactions between two given drugs are caused by their local chemical structures (substructures) and their DDI types are determined by the linkages between different substructure sets, we design a novel Substructure-aware Tensor Neural Network model for DDI prediction (STNN-DDI). The proposed model learns a 3-D tensor of $\langle $ substructure, substructure, interaction type $\rangle $ triplets, which characterizes a substructure-substructure interaction (SSI) space. According to a list of predefined substructures with specific chemical meanings, the mapping of drugs into this SSI space enables STNN-DDI to perform the multiple-type DDI prediction in both transductive and inductive scenarios in a unified form with an explicable manner. The comparison with deep learning-based state-of-the-art baselines demonstrates the superiority of STNN-DDI with the significant improvement of AUC, AUPR, Accuracy and Precision. More importantly, case studies illustrate its interpretability by both revealing an important substructure pair across drugs regarding a DDI type of interest and uncovering interaction type-specific substructure pairs in a given DDI. In summary, STNN-DDI provides an effective approach to predicting DDIs as well as explaining the interaction mechanisms among drugs. Source code is freely available at https://github.com/zsy-9/STNN-DDI.
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Affiliation(s)
- Hui Yu
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
| | - ShiYu Zhao
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
| | - JianYu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
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15
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Ren ZH, Yu CQ, Li LP, You ZH, Pan J, Guan YJ, Guo LX. BioChemDDI: Predicting Drug-Drug Interactions by Fusing Biochemical and Structural Information through a Self-Attention Mechanism. BIOLOGY 2022; 11:biology11050758. [PMID: 35625486 PMCID: PMC9138786 DOI: 10.3390/biology11050758] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/12/2022] [Accepted: 05/13/2022] [Indexed: 01/13/2023]
Abstract
Simple Summary Throughout history, combining drugs has been a common method in the fight against complex diseases. However, potential drug–drug interactions could give rise to unknown toxicity issues, which requires the urgent proposal of efficient methods to identify potential interactions.We use computer technology and machine learning techniques to propose a novel computational framework to calculate scores of drug–drug interaction probability for simplifying the screening process. Additionally, we built an online prescreening tool for biological researchers to further verify possible interactions in the fields of biomedicine and pharmacology. Overall, our study can provide new insights and approaches for rapidly identifying potential drug–drug interactions. Abstract During the development of drug and clinical applications, due to the co-administration of different drugs that have a high risk of interfering with each other’s mechanisms of action, correctly identifying potential drug–drug interactions (DDIs) is important to avoid a reduction in drug therapeutic activities and serious injuries to the organism. Therefore, to explore potential DDIs, we develop a computational method of integrating multi-level information. Firstly, the information of chemical sequence is fully captured by the Natural Language Processing (NLP) algorithm, and multiple biological function similarity information is fused by Similarity Network Fusion (SNF). Secondly, we extract deep network structure information through Hierarchical Representation Learning for Networks (HARP). Then, a highly representative comprehensive feature descriptor is constructed through the self-attention module that efficiently integrates biochemical and network features. Finally, a deep neural network (DNN) is employed to generate the prediction results. Contrasted with the previous supervision model, BioChemDDI innovatively introduced graph collapse for extracting a network structure and utilized the biochemical information during the pre-training process. The prediction results of the benchmark dataset indicate that BioChemDDI outperforms other existing models. Moreover, the case studies related to three cancer diseases, including breast cancer, hepatocellular carcinoma and malignancies, were analyzed using BioChemDDI. As a result, 24, 18 and 20 out of the top 30 predicted cancer-related drugs were confirmed by the databases. These experimental results demonstrate that BioChemDDI is a useful model to predict DDIs and can provide reliable candidates for biological experiments. The web server of BioChemDDI predictor is freely available to conduct further studies.
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Affiliation(s)
- Zhong-Hao Ren
- School of Information Engineering, Xijing University, Xi’an 710123, China; (Z.-H.R.); (Y.-J.G.); (L.-X.G.); (J.P.)
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi’an 710123, China; (Z.-H.R.); (Y.-J.G.); (L.-X.G.); (J.P.)
- Correspondence: (C.-Q.Y.); (L.-P.L.); Tel.: +86-189-9118-5758 (C.-Q.Y.); +86-173-9276-3836 (L.-P.L.)
| | - Li-Ping Li
- College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi 830052, China
- Correspondence: (C.-Q.Y.); (L.-P.L.); Tel.: +86-189-9118-5758 (C.-Q.Y.); +86-173-9276-3836 (L.-P.L.)
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China;
| | - Jie Pan
- School of Information Engineering, Xijing University, Xi’an 710123, China; (Z.-H.R.); (Y.-J.G.); (L.-X.G.); (J.P.)
| | - Yong-Jian Guan
- School of Information Engineering, Xijing University, Xi’an 710123, China; (Z.-H.R.); (Y.-J.G.); (L.-X.G.); (J.P.)
| | - Lu-Xiang Guo
- School of Information Engineering, Xijing University, Xi’an 710123, China; (Z.-H.R.); (Y.-J.G.); (L.-X.G.); (J.P.)
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Feng YH, Zhang SW. Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs. Molecules 2022; 27:molecules27093004. [PMID: 35566354 PMCID: PMC9105425 DOI: 10.3390/molecules27093004] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 04/28/2022] [Accepted: 04/30/2022] [Indexed: 12/04/2022] Open
Abstract
The treatment of complex diseases by using multiple drugs has become popular. However, drug-drug interactions (DDI) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. Therefore, for polypharmacy safety it is crucial to identify DDIs and explore their underlying mechanisms. The detection of DDI in the wet lab is expensive and time-consuming, due to the need for experimental research over a large volume of drug combinations. Although many computational methods have been developed to predict DDIs, most of these are incapable of predicting potential DDIs between drugs within the DDI network and new drugs from outside the DDI network. In addition, they are not designed to explore the underlying mechanisms of DDIs and lack interpretative capacity. Thus, here we propose a novel method of GNN-DDI to predict potential DDIs by constructing a five-layer graph attention network to identify k-hops low-dimensional feature representations for each drug from its chemical molecular graph, concatenating all identified features of each drug pair, and inputting them into a MLP predictor to obtain the final DDI prediction score. The experimental results demonstrate that our GNN-DDI is suitable for each of two DDI predicting scenarios, namely the potential DDIs among known drugs in the DDI network and those between drugs within the DDI network and new drugs from outside DDI network. The case study indicates that our method can explore the specific drug substructures that lead to the potential DDIs, which helps to improve interpretability and discover the underlying interaction mechanisms of drug pairs.
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Vo TH, Nguyen NTK, Kha QH, Le NQK. On the road to explainable AI in drug-drug interactions prediction: a systematic review. Comput Struct Biotechnol J 2022; 20:2112-2123. [PMID: 35832629 PMCID: PMC9092071 DOI: 10.1016/j.csbj.2022.04.021] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/15/2022] [Accepted: 04/15/2022] [Indexed: 12/26/2022] Open
Abstract
A systematic review on applications of explainable AI in drug-drug interaction prediction. Review is conducted on a comprehensive set of 94 papers from five prestigious databases. Discussions on the promises and challenges of explainable AI algorithms for drug-drug interaction prediction.
Over the past decade, polypharmacy instances have been common in multi-diseases treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected adverse drug events (ADEs) in multiple regimens therapy remain a significant issue. Since artificial intelligence (AI) is ubiquitous today, many AI prediction models have been developed to predict DDIs to support clinicians in pharmacotherapy-related decisions. However, even though DDI prediction models have great potential for assisting physicians in polypharmacy decisions, there are still concerns regarding the reliability of AI models due to their black-box nature. Building AI models with explainable mechanisms can augment their transparency to address the above issue. Explainable AI (XAI) promotes safety and clarity by showing how decisions are made in AI models, especially in critical tasks like DDI predictions. In this review, a comprehensive overview of AI-based DDI prediction, including the publicly available source for AI-DDIs studies, the methods used in data manipulation and feature preprocessing, the XAI mechanisms to promote trust of AI, especially for critical tasks as DDIs prediction, the modeling methods, is provided. Limitations and the future directions of XAI in DDIs are also discussed.
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Affiliation(s)
- Thanh Hoa Vo
- Master Program in Clinical Genomics and Proteomics, College of Pharmacy, Taipei Medical University, Taipei 110, Taiwan
| | - Ngan Thi Kim Nguyen
- School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan
| | - Quang Hien Kha
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
- Corresponding author at: Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan.
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18
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Han K, Cao P, Wang Y, Xie F, Ma J, Yu M, Wang J, Xu Y, Zhang Y, Wan J. A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning. Front Pharmacol 2022; 12:814858. [PMID: 35153767 PMCID: PMC8835726 DOI: 10.3389/fphar.2021.814858] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 12/20/2021] [Indexed: 01/01/2023] Open
Abstract
Drug–drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug–drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.
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Affiliation(s)
- Ke Han
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
- College of Pharmacy, Harbin University of Commerce, Harbin, China
- *Correspondence: Ke Han, ; Jie Wan,
| | - Peigang Cao
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Yu Wang
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Fang Xie
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Jiaqi Ma
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Mengyao Yu
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Jianchun Wang
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Yaoqun Xu
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Yu Zhang
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Jie Wan
- Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin, China
- *Correspondence: Ke Han, ; Jie Wan,
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Ou-Yang L, Lu F, Zhang ZC, Wu M. Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey. Brief Bioinform 2021; 23:6447434. [PMID: 34864871 DOI: 10.1093/bib/bbab479] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/25/2021] [Accepted: 10/18/2021] [Indexed: 02/02/2023] Open
Abstract
Advances in high-throughput experimental technologies promote the accumulation of vast number of biomedical data. Biomedical link prediction and single-cell RNA-sequencing (scRNA-seq) data imputation are two essential tasks in biomedical data analyses, which can facilitate various downstream studies and gain insights into the mechanisms of complex diseases. Both tasks can be transformed into matrix completion problems. For a variety of matrix completion tasks, matrix factorization has shown promising performance. However, the sparseness and high dimensionality of biomedical networks and scRNA-seq data have raised new challenges. To resolve these issues, various matrix factorization methods have emerged recently. In this paper, we present a comprehensive review on such matrix factorization methods and their usage in biomedical link prediction and scRNA-seq data imputation. Moreover, we select representative matrix factorization methods and conduct a systematic empirical comparison on 15 real data sets to evaluate their performance under different scenarios. By summarizing the experimental results, we provide general guidelines for selecting matrix factorization methods for different biomedical matrix completion tasks and point out some future directions to further improve the performance for biomedical link prediction and scRNA-seq data imputation.
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Affiliation(s)
- Le Ou-Yang
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Media Security, and Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China.,Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen,518172, China
| | - Fan Lu
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Media Security, and Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Zi-Chao Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Min Wu
- Institute for Infocomm Research (I2R), A*STAR, 138632, Singapore
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20
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Schwarz K, Allam A, Perez Gonzalez NA, Krauthammer M. AttentionDDI: Siamese attention-based deep learning method for drug-drug interaction predictions. BMC Bioinformatics 2021; 22:412. [PMID: 34418954 PMCID: PMC8379737 DOI: 10.1186/s12859-021-04325-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 07/13/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Drug-drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves. Due to the massive number of possible drug pairs, it is nearly impossible to experimentally test all combinations and discover previously unobserved side effects. Therefore, machine learning based methods are being used to address this issue. METHODS We propose a Siamese self-attention multi-modal neural network for DDI prediction that integrates multiple drug similarity measures that have been derived from a comparison of drug characteristics including drug targets, pathways and gene expression profiles. RESULTS Our proposed DDI prediction model provides multiple advantages: (1) It is trained end-to-end, overcoming limitations of models composed of multiple separate steps, (2) it offers model explainability via an Attention mechanism for identifying salient input features and (3) it achieves similar or better prediction performance (AUPR scores ranging from 0.77 to 0.92) compared to state-of-the-art DDI models when tested on various benchmark datasets. Novel DDI predictions are further validated using independent data resources. CONCLUSIONS We find that a Siamese multi-modal neural network is able to accurately predict DDIs and that an Attention mechanism, typically used in the Natural Language Processing domain, can be beneficially applied to aid in DDI model explainability.
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Affiliation(s)
- Kyriakos Schwarz
- Department of Quantitative Biomedicine, University of Zurich, Schmelzbergstrasse 26, 8006 Zurich, Switzerland
- Biomedical Informatics, University Hospital of Zurich, Zurich, Switzerland
| | - Ahmed Allam
- Department of Quantitative Biomedicine, University of Zurich, Schmelzbergstrasse 26, 8006 Zurich, Switzerland
- Biomedical Informatics, University Hospital of Zurich, Zurich, Switzerland
| | - Nicolas Andres Perez Gonzalez
- Department of Quantitative Biomedicine, University of Zurich, Schmelzbergstrasse 26, 8006 Zurich, Switzerland
- Biomedical Informatics, University Hospital of Zurich, Zurich, Switzerland
| | - Michael Krauthammer
- Department of Quantitative Biomedicine, University of Zurich, Schmelzbergstrasse 26, 8006 Zurich, Switzerland
- Biomedical Informatics, University Hospital of Zurich, Zurich, Switzerland
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21
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ADDI: Recommending alternatives for drug-drug interactions with negative health effects. Comput Biol Med 2020; 125:103969. [PMID: 32836102 DOI: 10.1016/j.compbiomed.2020.103969] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 08/09/2020] [Accepted: 08/09/2020] [Indexed: 11/21/2022]
Abstract
Investigating the interactions among various drugs is an indispensable issue in the field of computational biology. Scientific literature represents a rich source for the retrieval of knowledge about the interactions between drugs. Predicting drug-drug interaction (DDI) types will help biologists to evade hazardous drug interactions and support them in discovering potential alternatives that increase therapeutic efficacy and reduce toxicity. In this paper, we propose a general-purpose method called ADDI (standing for Alternative Drug-Drug Interaction) that applies deep learning on PubMed abstracts to predict interaction types among drugs. As an application, ADDI recommends alternatives for drug-drug interactions (DDIs) which have Negative Health Effects Types (NHETs). ADDI clearly outperforms state-of-the-art methods, on average by 13%, with respect to accuracy by using only the textual content of the online PubMed papers. Additionally, manual evaluation of ADDI indicates high precision in recommending alternatives for DDIs with NHETs.
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