1
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Biswal S, Mallick B. Unlocking the potential of signature-based drug repurposing for anticancer drug discovery. Arch Biochem Biophys 2024; 761:110150. [PMID: 39265695 DOI: 10.1016/j.abb.2024.110150] [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: 04/21/2024] [Revised: 08/01/2024] [Accepted: 09/09/2024] [Indexed: 09/14/2024]
Abstract
Cancer is the leading cause of death worldwide and is often associated with tumor relapse even after chemotherapeutics. This reveals malignancy is a complex process, and high-throughput omics strategies in recent years have contributed significantly in decoding the molecular mechanisms of these complex events in cancer. Further, the omics studies yield a large volume of cancer-specific molecular signatures that promote the discovery of cancer therapy drugs by a method termed signature-based drug repurposing. The drug repurposing method identifies new uses for approved drugs beyond their intended initial therapeutic use, and there are several approaches to it. In this review, we discuss signature-based drug repurposing in cancer, how cancer omics have revolutionized this method of drug discovery, and how one can use the cancer signature data for repurposed drug identification by providing a step-by-step procedural handout. This modern approach maximizes the use of existing therapeutic agents for cancer therapy or combination therapy to overcome chemotherapeutics resistance, making it a pragmatic and efficient alternative to traditional resource-intensive and time-consuming methods.
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Affiliation(s)
- Sruti Biswal
- RNAi and Functional Genomics Lab., Department of Life Science, National Institute of Technology Rourkela, Rourkela-769008, Odisha, India
| | - Bibekanand Mallick
- RNAi and Functional Genomics Lab., Department of Life Science, National Institute of Technology Rourkela, Rourkela-769008, Odisha, India.
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2
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Wen S, Liu Y, Yang G, Chen W, Wu H, Zhu X, Wang Y. A method for miRNA diffusion association prediction using machine learning decoding of multi-level heterogeneous graph Transformer encoded representations. Sci Rep 2024; 14:20490. [PMID: 39227405 PMCID: PMC11371806 DOI: 10.1038/s41598-024-68897-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 07/29/2024] [Indexed: 09/05/2024] Open
Abstract
MicroRNAs (miRNAs) are a key class of endogenous non-coding RNAs that play a pivotal role in regulating diseases. Accurately predicting the intricate relationships between miRNAs and diseases carries profound implications for disease diagnosis, treatment, and prevention. However, these prediction tasks are highly challenging due to the complexity of the underlying relationships. While numerous effective prediction models exist for validating these associations, they often encounter information distortion due to limitations in efficiently retaining information during the encoding-decoding process. Inspired by Multi-layer Heterogeneous Graph Transformer and Machine Learning XGboost classifier algorithm, this study introduces a novel computational approach based on multi-layer heterogeneous encoder-machine learning decoder structure for miRNA-disease association prediction (MHXGMDA). First, we employ the multi-view similarity matrices as the input coding for MHXGMDA. Subsequently, we utilize the multi-layer heterogeneous encoder to capture the embeddings of miRNAs and diseases, aiming to capture the maximum amount of relevant features. Finally, the information from all layers is concatenated to serve as input to the machine learning classifier, ensuring maximal preservation of encoding details. We conducted a comprehensive comparison of seven different classifier models and ultimately selected the XGBoost algorithm as the decoder. This algorithm leverages miRNA embedding features and disease embedding features to decode and predict the association scores between miRNAs and diseases. We applied MHXGMDA to predict human miRNA-disease associations on two benchmark datasets. Experimental findings demonstrate that our approach surpasses several leading methods in terms of both the area under the receiver operating characteristic curve and the area under the precision-recall curve.
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Affiliation(s)
- SiJian Wen
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China
| | - YinBo Liu
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China
| | - Guang Yang
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China
| | - WenXi Chen
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China
| | - HaiTao Wu
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China
| | - XiaoLei Zhu
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China.
| | - YongMei Wang
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China.
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Hefei, 230036, China.
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3
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Li G, Li S, Liang C, Xiao Q, Luo J. Drug repositioning based on residual attention network and free multiscale adversarial training. BMC Bioinformatics 2024; 25:261. [PMID: 39118000 PMCID: PMC11308596 DOI: 10.1186/s12859-024-05893-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/06/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment. However, reliable detection of drug-disease relationships via traditional methods is costly and slow. Therefore, investigations into computational methods for predicting drug-disease associations are currently needed. RESULTS This paper presents a novel drug-disease association prediction method, RAFGAE. First, RAFGAE integrates known associations between diseases and drugs into a bipartite network. Second, RAFGAE designs the Re_GAT framework, which includes multilayer graph attention networks (GATs) and two residual networks. The multilayer GATs are utilized for learning the node embeddings, which is achieved by aggregating information from multihop neighbors. The two residual networks are used to alleviate the deep network oversmoothing problem, and an attention mechanism is introduced to combine the node embeddings from different attention layers. Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature quality through small gradient adversarial perturbation iterations, improving the prediction performance. Finally, tenfold cross-validations on two benchmark datasets show that RAFGAE outperforms current methods. In addition, case studies have confirmed that RAFGAE can detect novel drug-disease associations. CONCLUSIONS The comprehensive experimental results validate the utility and accuracy of RAFGAE. We believe that this method may serve as an excellent predictor for identifying unobserved disease-drug associations.
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Affiliation(s)
- Guanghui Li
- School of Information Engineering, East China Jiaotong University, Nanchang, China.
| | - Shuwen Li
- School of Information Engineering, East China Jiaotong University, Nanchang, China
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Qiu Xiao
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
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4
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Ma C, Gurkan-Cavusoglu E. A comprehensive review of computational cell cycle models in guiding cancer treatment strategies. NPJ Syst Biol Appl 2024; 10:71. [PMID: 38969664 PMCID: PMC11226463 DOI: 10.1038/s41540-024-00397-7] [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: 01/26/2024] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.
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Affiliation(s)
- Chenhui Ma
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Evren Gurkan-Cavusoglu
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA
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5
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Huan JM, Wang XJ, Li Y, Zhang SJ, Hu YL, Li YL. The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of real-world clinical data. BioData Min 2024; 17:13. [PMID: 38773619 PMCID: PMC11110203 DOI: 10.1186/s13040-024-00365-1] [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: 06/28/2023] [Accepted: 05/17/2024] [Indexed: 05/24/2024] Open
Abstract
A knowledge graph can effectively showcase the essential characteristics of data and is increasingly emerging as a significant means of integrating information in the field of artificial intelligence. Coronary artery plaque represents a significant etiology of cardiovascular events, posing a diagnostic challenge for clinicians who are confronted with a multitude of nonspecific symptoms. To visualize the hierarchical relationship network graph of the molecular mechanisms underlying plaque properties and symptom phenotypes, patient symptomatology was extracted from electronic health record data from real-world clinical settings. Phenotypic networks were constructed utilizing clinical data and protein‒protein interaction networks. Machine learning techniques, including convolutional neural networks, Dijkstra's algorithm, and gene ontology semantic similarity, were employed to quantify clinical and biological features within the network. The resulting features were then utilized to train a K-nearest neighbor model, yielding 23 symptoms, 41 association rules, and 61 hub genes across the three types of plaques studied, achieving an area under the curve of 92.5%. Weighted correlation network analysis and pathway enrichment were subsequently utilized to identify lipid status-related genes and inflammation-associated pathways that could help explain the differences in plaque properties. To confirm the validity of the network graph model, we conducted coexpression analysis of the hub genes to evaluate their potential diagnostic value. Additionally, we investigated immune cell infiltration, examined the correlations between hub genes and immune cells, and validated the reliability of the identified biological pathways. By integrating clinical data and molecular network information, this biomedical knowledge graph model effectively elucidated the potential molecular mechanisms that collude symptoms, diseases, and molecules.
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Affiliation(s)
- Jia-Ming Huan
- First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Xiao-Jie Wang
- First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Yuan Li
- First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Shi-Jun Zhang
- First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Yuan-Long Hu
- First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Yun-Lun Li
- First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China.
- Department of Cardiovascular, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, China.
- Precision Diagnosis and Treatment of Cardiovascular Diseases with Traditional Chinese Medicine Shandong Engineering Research Center, Jinan, 250355, China.
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6
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Park JH, Cho YR. Computational drug repositioning with attention walking. Sci Rep 2024; 14:10072. [PMID: 38698208 PMCID: PMC11066070 DOI: 10.1038/s41598-024-60756-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 04/26/2024] [Indexed: 05/05/2024] Open
Abstract
Drug repositioning aims to identify new therapeutic indications for approved medications. Recently, the importance of computational drug repositioning has been highlighted because it can reduce the costs, development time, and risks compared to traditional drug discovery. Most approaches in this area use networks for systematic analysis. Inferring drug-disease associations is then defined as a link prediction problem in a heterogeneous network composed of drugs and diseases. In this article, we present a novel method of computational drug repositioning, named drug repositioning with attention walking (DRAW). DRAW proceeds as follows: first, a subgraph enclosing the target link for prediction is extracted. Second, a graph convolutional network captures the structural features of the labeled nodes in the subgraph. Third, the transition probabilities are computed using attention mechanisms and converted into random walk profiles. Finally, a multi-layer perceptron takes random walk profiles and predicts whether a target link exists. As an experiment, we constructed two heterogeneous networks with drug-drug similarities based on chemical structures and anatomical therapeutic chemical classification (ATC) codes. Using 10-fold cross-validation, DRAW achieved an area under the receiver operating characteristic (ROC) curve of 0.903 and outperformed state-of-the-art methods. Moreover, we demonstrated the results of case studies for selected drugs and diseases to further confirm the capability of DRAW to predict drug-disease associations.
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Affiliation(s)
- Jong-Hoon Park
- Division of Software, Yonsei University Mirae Campus, Wonju-si, 26493, Gangwon-do, Korea
| | - Young-Rae Cho
- Division of Software, Yonsei University Mirae Campus, Wonju-si, 26493, Gangwon-do, Korea.
- Division of Digital Healthcare, Yonsei University Mirae Campus, Wonju-si, 26493, Gangwon-do, Korea.
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7
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Wang Y, Song J, Dai Q, Duan X. Hierarchical Negative Sampling Based Graph Contrastive Learning Approach for Drug-Disease Association Prediction. IEEE J Biomed Health Inform 2024; 28:3146-3157. [PMID: 38294927 DOI: 10.1109/jbhi.2024.3360437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
Abstract
Predicting potential drug-disease associations (RDAs) plays a pivotal role in elucidating therapeutic strategies for diseases and facilitating drug repositioning, making it of paramount importance. However, existing methods are constrained and rely heavily on limited domain-specific knowledge, impeding their ability to effectively predict candidate associations between drugs and diseases. Moreover, the simplistic definition of unknown information pertaining to drug-disease relationships as negative samples presents inherent limitations. To overcome these challenges, we introduce a novel hierarchical negative sampling-based graph contrastive model, termed HSGCLRDA, which aims to forecast latent associations between drugs and diseases. In this study, HSGCLRDA integrates the association information as well as similarity between drugs, diseases and proteins. Meanwhile, the model constructs a drug-disease-protein heterogeneous network. Subsequently, employing a hierarchical structural sampling technique, we establish reliable negative drug-disease samples utilizing PageRank algorithms. Utilizing meta-path aggregation within the heterogeneous network, we derive low-dimensional representations for drugs and diseases, thereby constructing global and local feature graphs that capture their interactions comprehensively. To obtain representation information, we adopt a self-supervised graph contrastive approach that leverages graph convolutional networks (GCNs) and second-order GCNs to extract feature graph information. Furthermore, we integrate a contrastive cost function derived from the cross-entropy cost function, facilitating holistic model optimization. Experimental results obtained from benchmark datasets not only showcase the superior performance of HSGCLRDA compared to various baseline methods in predicting RDAs but also emphasize its practical utility in identifying novel potential diseases associated with existing drugs through meticulous case studies.
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8
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Yang R, Fu Y, Zhang Q, Zhang L. GCNGAT: Drug-disease association prediction based on graph convolution neural network and graph attention network. Artif Intell Med 2024; 150:102805. [PMID: 38553169 DOI: 10.1016/j.artmed.2024.102805] [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: 03/31/2023] [Revised: 01/22/2024] [Accepted: 02/08/2024] [Indexed: 04/02/2024]
Abstract
Predicting drug-disease associations can contribute to discovering new therapeutic potentials of drugs, and providing important association information for new drug research and development. Many existing drug-disease association prediction methods have not distinguished relevant background information for the same drug targeted to different diseases. Therefore, this paper proposes a drug-disease association prediction model based on graph convolutional network and graph attention network (GCNGAT) to reposition marketed drugs under the distinguishment of background information. Firstly, in order to obtain initial drug-disease information, a drug-disease heterogeneous graph structure is constructed based on all known drug-disease associations. Secondly, based on the heterogeneous graph structure, the corresponding subgraphs of each group of drug-disease association pairs are extracted to distinguish different background information for the same drug from different diseases. Finally, a model combining Graph neural network with global Average pooling (GnnAp) is designed to predict potential drug-disease associations by learning drug-disease interaction feature representations. The experimental results show that adding subgraph extraction can effectively improve the prediction performance of the model, and the graph representation learning module can fully extract the deep features of drug-disease. Using the 5-fold cross-validation, the proposed model (GCNGAT) achieves AUC (Area Under the receiver operating characteristic Curve) values of 0.9182 and 0.9417 on the PREDICT dataset and CDataset dataset, respectively. Compared with other predictors on the same dataset (PREDICT dataset), GCNGAT outperforms the existing best-performing model (PSGCN), with a 1.58% increase in the AUC value. It is anticipated that this model can provide experimental reference for drug repositioning and further promote the drug research and development process.
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Affiliation(s)
- Runtao Yang
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China.
| | - Yao Fu
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China.
| | - Qian Zhang
- Heze Institute of Science and Technology Information, Heze, 274000, China.
| | - Lina Zhang
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China.
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9
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Li Y, Yang Y, Tong Z, Wang Y, Mi Q, Bai M, Liang G, Li B, Shu K. A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods. Brief Bioinform 2024; 25:bbae172. [PMID: 38647153 PMCID: PMC11033846 DOI: 10.1093/bib/bbae172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 02/25/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.
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Affiliation(s)
- Yinghong Li
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Yinqi Yang
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Zhuohao Tong
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Yu Wang
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Qin Mi
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Mingze Bai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, P. R. China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, P. R. China
| | - Kunxian Shu
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
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10
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Luo H, Zhu C, Wang J, Zhang G, Luo J, Yan C. Prediction of drug-disease associations based on reinforcement symmetric metric learning and graph convolution network. Front Pharmacol 2024; 15:1337764. [PMID: 38384286 PMCID: PMC10879308 DOI: 10.3389/fphar.2024.1337764] [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: 11/13/2023] [Accepted: 01/18/2024] [Indexed: 02/23/2024] Open
Abstract
Accurately identifying novel indications for drugs is crucial in drug research and discovery. Traditional drug discovery is costly and time-consuming. Computational drug repositioning can provide an effective strategy for discovering potential drug-disease associations. However, the known experimentally verified drug-disease associations is relatively sparse, which may affect the prediction performance of the computational drug repositioning methods. Moreover, while the existing drug-disease prediction method based on metric learning algorithm has achieved better performance, it simply learns features of drugs and diseases only from the drug-centered perspective, and cannot comprehensively model the latent features of drugs and diseases. In this study, we propose a novel drug repositioning method named RSML-GCN, which applies graph convolutional network and reinforcement symmetric metric learning to predict potential drug-disease associations. RSML-GCN first constructs a drug-disease heterogeneous network by integrating the association and feature information of drugs and diseases. Then, the graph convolutional network (GCN) is applied to complement the drug-disease association information. Finally, reinforcement symmetric metric learning with adaptive margin is designed to learn the latent vector representation of drugs and diseases. Based on the learned latent vector representation, the novel drug-disease associations can be identified by the metric function. Comprehensive experiments on benchmark datasets demonstrated the superior prediction performance of RSML-GCN for drug repositioning.
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Affiliation(s)
- Huimin Luo
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China
| | - Chunli Zhu
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China
| | - Jianlin Wang
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China
| | - Ge Zhang
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China
| | - Junwei Luo
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Chaokun Yan
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China
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11
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Li D, Xiao Z, Sun H, Jiang X, Zhao W, Shen X. Prediction of Drug-Disease Associations Based on Multi-Kernel Deep Learning Method in Heterogeneous Graph Embedding. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:120-128. [PMID: 38051617 DOI: 10.1109/tcbb.2023.3339189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Computational drug repositioning can identify potential associations between drugs and diseases. This technology has been shown to be effective in accelerating drug development and reducing experimental costs. Although there has been plenty of research for this task, existing methods are deficient in utilizing complex relationships among biological entities, which may not be conducive to subsequent simulation of drug treatment processes. In this article, we propose a heterogeneous graph embedding method called HMLKGAT to infer novel potential drugs for diseases. More specifically, we first construct a heterogeneous information network by combining drug-disease, drug-protein and disease-protein biological networks. Then, a multi-layer graph attention model is utilized to capture the complex associations in the network to derive representations for drugs and diseases. Finally, to maintain the relationship of nodes in different feature spaces, we propose a multi-kernel learning method to transform and combine the representations. Experimental results demonstrate that HMLKGAT outperforms six state-of-the-art methods in drug-related disease prediction, and case studies of five classical drugs further demonstrate the effectiveness of HMLKGAT.
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12
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Shtar G, Solomon A, Mazuz E, Rokach L, Shapira B. A simplified similarity-based approach for drug-drug interaction prediction. PLoS One 2023; 18:e0293629. [PMID: 37943768 PMCID: PMC10635435 DOI: 10.1371/journal.pone.0293629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/17/2023] [Indexed: 11/12/2023] Open
Abstract
Drug-drug interactions (DDIs) are a critical component of drug safety surveillance. Laboratory studies aimed at detecting DDIs are typically difficult, expensive, and time-consuming; therefore, developing in-silico methods is critical. Machine learning-based approaches for DDI prediction have been developed; however, in many cases, their ability to achieve high accuracy relies on data only available towards the end of the molecule lifecycle. Here, we propose a simple yet effective similarity-based method for preclinical DDI prediction where only the chemical structure is available. We test the model on new, unseen drugs. To focus on the preclinical problem setting, we conducted a retrospective analysis and tested the models on drugs that were added to a later version of the DrugBank database. We extend an existing method, adjacency matrix factorization with propagation (AMFP), to support unseen molecules by applying a new lookup mechanism to the drugs' chemical structure, lookup adjacency matrix factorization with propagation (LAMFP). We show that using an ensemble of different similarity measures improves the results. We also demonstrate that Chemprop, a message-passing neural network, can be used for DDI prediction. In computational experiments, LAMFP results in high accuracy, with an area under the receiver operating characteristic curve of 0.82 for interactions involving a new drug and an existing drug and for interactions involving only existing drugs. Moreover, LAMFP outperforms state-of-the-art, complex graph neural network DDI prediction methods.
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Affiliation(s)
- Guy Shtar
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Department of Information Systems, University of Haifa, Haifa, Israel
| | - Adir Solomon
- Department of Information Systems, University of Haifa, Haifa, Israel
| | - Eyal Mazuz
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Lior Rokach
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Bracha Shapira
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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13
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Wang S, Li J, Wang D, Xu D, Jin J, Wang Y. Predicting Drug-Disease Associations Through Similarity Network Fusion and Multi-View Feature Projection Representation. IEEE J Biomed Health Inform 2023; 27:5165-5176. [PMID: 37527303 DOI: 10.1109/jbhi.2023.3300717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
Predicting drug-disease associations (DDAs) through computational methods has become a prevalent trend in drug development because of their high efficiency and low cost. Existing methods usually focus on constructing heterogeneous networks by collecting multiple data resources to improve prediction ability. However, potential association possibilities of numerous unconfirmed drug-related or disease-related pairs are not sufficiently considered. In this article, we propose a novel computational model to predict new DDAs. First, a heterogeneous network is constructed, including four types of nodes (drugs, targets, cell lines, diseases) and three types of edges (associations, association scores, similarities). Second, an updating and merging-based similarity network fusion method, termed UM-SF, is presented to fuse various similarity networks with diverse weights. Finally, an intermediate layer-mediated multi-view feature projection representation method, termed IM-FP, is proposed to calculate the predicted DDA scores. This method uses multiple association scores to construct multi-view drug features, then projects them into disease space through the intermediate layer, where an intermediate layer similarity constraint is designed to learn the projection matrices. Results of comparative experiments reveal the effectiveness of our innovations. Comparisons with other state-of-the-art models by the 10-fold cross-validation experiment indicate our model's advantage on AUROC and AUPR metrics. Moreover, our proposed model successfully predicted 107 novel high-ranked DDAs.
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Ye S, Zhao W, Shen X, Jiang X, He T. An effective multi-task learning framework for drug repurposing based on graph representation learning. Methods 2023; 218:48-56. [PMID: 37516260 DOI: 10.1016/j.ymeth.2023.07.008] [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: 02/20/2023] [Revised: 07/04/2023] [Accepted: 07/20/2023] [Indexed: 07/31/2023] Open
Abstract
Drug repurposing, which typically applies the procedure of drug-disease associations (DDAs) prediction, is a feasible solution to drug discovery. Compared with traditional methods, drug repurposing can reduce the cost and time for drug development and advance the success rate of drug discovery. Although many methods for drug repurposing have been proposed and the obtained results are relatively acceptable, there is still some room for improving the predictive performance, since those methods fail to consider fully the issue of sparseness in known drug-disease associations. In this paper, we propose a novel multi-task learning framework based on graph representation learning to identify DDAs for drug repurposing. In our proposed framework, a heterogeneous information network is first constructed by combining multiple biological datasets. Then, a module consisting of multiple layers of graph convolutional networks is utilized to learn low-dimensional representations of nodes in the constructed heterogeneous information network. Finally, two types of auxiliary tasks are designed to help to train the target task of DDAs prediction in the multi-task learning framework. Comprehensive experiments are conducted on real data and the results demonstrate the effectiveness of the proposed method for drug repurposing.
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Affiliation(s)
- Shengwei Ye
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China
| | - Weizhong Zhao
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China.
| | - Xianjun Shen
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China
| | - Xingpeng Jiang
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China
| | - Tingting He
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China
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Yang M, Yang B, Duan G, Wang J. ITRPCA: a new model for computational drug repositioning based on improved tensor robust principal component analysis. Front Genet 2023; 14:1271311. [PMID: 37795241 PMCID: PMC10545866 DOI: 10.3389/fgene.2023.1271311] [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: 08/02/2023] [Accepted: 08/23/2023] [Indexed: 10/06/2023] Open
Abstract
Background: Drug repositioning is considered a promising drug development strategy with the goal of discovering new uses for existing drugs. Compared with the experimental screening for drug discovery, computational drug repositioning offers lower cost and higher efficiency and, hence, has become a hot issue in bioinformatics. However, there are sparse samples, multi-source information, and even some noises, which makes it difficult to accurately identify potential drug-associated indications. Methods: In this article, we propose a new scheme with improved tensor robust principal component analysis (ITRPCA) in multi-source data to predict promising drug-disease associations. First, we use a weighted k-nearest neighbor (WKNN) approach to increase the overall density of the drug-disease association matrix that will assist in prediction. Second, a drug tensor with five frontal slices and a disease tensor with two frontal slices are constructed using multi-similarity matrices and an updated association matrix. The two target tensors naturally integrate multiple sources of data from the drug-side aspect and the disease-side aspect, respectively. Third, ITRPCA is employed to isolate the low-rank tensor and noise information in the tensor. In this step, an additional range constraint is incorporated to ensure that all the predicted entry values of a low-rank tensor are within the specific interval. Finally, we focus on identifying promising drug indications by analyzing drug-disease association pairs derived from the low-rank drug and low-rank disease tensors. Results: We evaluate the effectiveness of the ITRPCA method by comparing it with five prominent existing drug repositioning methods. This evaluation is carried out using 10-fold cross-validation and independent testing experiments. Our numerical results show that ITRPCA not only yields higher prediction accuracy but also exhibits remarkable computational efficiency. Furthermore, case studies demonstrate the practical effectiveness of our method.
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Affiliation(s)
- Mengyun Yang
- School of Mechanical and Energy Engineering, Shaoyang University, Shaoyang, China
- School of Computer Science, Hunan First Normal University, Changsha, China
| | - Bin Yang
- School of Mechanical and Energy Engineering, Shaoyang University, Shaoyang, China
| | - Guihua Duan
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, China
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Yang X, Yang G, Chu J. Self-Supervised Learning for Label Sparsity in Computational Drug Repositioning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3245-3256. [PMID: 37028367 DOI: 10.1109/tcbb.2023.3254163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The computational drug repositioning aims to discover new uses for marketed drugs, which can accelerate the drug development process and play an important role in the existing drug discovery system. However, the number of validated drug-disease associations is scarce compared to the number of drugs and diseases in the real world. Too few labeled samples will make the classification model unable to learn effective latent factors of drugs, resulting in poor generalization performance. In this work, we propose a multi-task self-supervised learning framework for computational drug repositioning. The framework tackles label sparsity by learning a better drug representation. Specifically, we take the drug-disease association prediction problem as the main task, and the auxiliary task is to use data augmentation strategies and contrast learning to mine the internal relationships of the original drug features, so as to automatically learn a better drug representation without supervised labels. And through joint training, it is ensured that the auxiliary task can improve the prediction accuracy of the main task. More precisely, the auxiliary task improves drug representation and serving as additional regularization to improve generalization. Furthermore, we design a multi-input decoding network to improve the reconstruction ability of the autoencoder model. We evaluate our model using three real-world datasets. The experimental results demonstrate the effectiveness of the multi-task self-supervised learning framework, and its predictive ability is superior to the state-of-the-art model.
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Zhao H, Duan G, Ni P, Yan C, Li Y, Wang J. RNPredATC: A Deep Residual Learning-Based Model With Applications to the Prediction of Drug-ATC Code Association. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2712-2723. [PMID: 34110998 DOI: 10.1109/tcbb.2021.3088256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The Anatomical Therapeutic Chemical (ATC) classification system, designated by the World Health Organization Collaborating Center (WHOCC), has been widely used in drug screening, repositioning, and similarity research. The ATC classification system assigns different codes to drugs according to the organ or system on which they act and/or their therapeutic and chemical characteristics. Correctly identifying the potential ATC codes for drugs can accelerate drug development and reduce the cost of experiments. Several classifiers have been proposed in this regard. However, they lack of ability to learn basic features from sparsely known drug-ATC code associations. Therefore, there is an urgent need for novel computational methods to precisely predict potential drug-ATC code associations in multiple levels of the ATC classification system based on known associations between drugs and ATC codes. In this paper, we provide a novel end-to-end model, so-called RNPredATC, to predict potential drug-ATC code associations in five ATC classification levels. RNPredATC can extract dense feature vectors from sparsely known drug-ATC code associations and reduce the impact from the degradation problem by a novel deep residual learning. We extensively compare our method with some state-of-the-art methods, including NetPredATC, SPACE, and some multi-label-based methods. Our experimental results show that RNPredATC achieves better performances in five-fold and ten-fold cross validations. Furthermore, the visualization analysis of hidden layers and case studies of predicted associations at the fifth ATC classification level confirm that RNPredATC can effectively identify the potential ATC codes of drugs.
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18
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Zhu X, Lu W. Multi-Label Classification With Dual Tail-Node Augmentation for Drug Repositioning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3068-3079. [PMID: 37418410 DOI: 10.1109/tcbb.2023.3292883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
Due to the lengthy and costly process of new drug discovery, increasing attention has been paid to drug repositioning, i.e., identifying new drug-disease associations. Current machine learning methods for drug repositioning mainly leverage matrix factorization or graph neural networks, and have achieved impressive performance. However, they often suffer from insufficient training labels of inter-domain associations, while ignore the intra-domain associations. Moreover, they often neglect the importance of tail nodes that have few known associations, which limits their effectiveness in drug repositioning. In this paper, we propose a novel multi-label classification model with dual Tail-Node Augmentation for Drug Repositioning (TNA-DR). We incorporate disease-disease similarity and drug-drug similarity information into k-nearest neighbor ( kNN) augmentation module and contrastive augmentation module, respectively, which effectively complements the weak supervision of drug-disease associations. Furthermore, before employing the two augmentation modules, we filter the nodes by their degrees, so that the two modules are only applied to tail nodes. We conduct 10-fold cross validation experiments on four different real-world datasets, and our model achieves the state-of-the-art performance on all the four datasets. We also demonstrate our model's capability of identifying drug candidates for new diseases and discovering potential new links between existing drugs and diseases.
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Kim Y, Cho YR. Predicting Drug-Gene-Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning. Biomedicines 2023; 11:1998. [PMID: 37509637 PMCID: PMC10377142 DOI: 10.3390/biomedicines11071998] [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: 06/20/2023] [Revised: 07/07/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023] Open
Abstract
Drug repositioning offers the significant advantage of greatly reducing the cost and time of drug discovery by identifying new therapeutic indications for existing drugs. In particular, computational approaches using networks in drug repositioning have attracted attention for inferring potential associations between drugs and diseases efficiently based on the network connectivity. In this article, we proposed a network-based drug repositioning method to construct a drug-gene-disease tensor by integrating drug-disease, drug-gene, and disease-gene associations and predict drug-gene-disease triple associations through tensor decomposition. The proposed method, which ensembles generalized tensor decomposition (GTD) and multi-layer perceptron (MLP), models drug-gene-disease associations through GTD and learns the features of drugs, genes, and diseases through MLP, providing more flexibility and non-linearity than conventional tensor decomposition. We experimented with drug-gene-disease association prediction using two distinct networks created by chemical structures and ATC codes as drug features. Moreover, we leveraged drug, gene, and disease latent vectors obtained from the predicted triple associations to predict drug-disease, drug-gene, and disease-gene pairwise associations. Our experimental results revealed that the proposed ensemble method was superior for triple association prediction. The ensemble model achieved an AUC of 0.96 in predicting triple associations for new drugs, resulting in an approximately 7% improvement over the performance of existing models. It also showed competitive accuracy for pairwise association prediction compared with previous methods. This study demonstrated that incorporating genetic information leads to notable advancements in drug repositioning.
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Affiliation(s)
- Yoonbee Kim
- Division of Software, Yonsei University Mirae Campus, Wonju-si 26493, Gangwon-do, Republic of Korea
| | - Young-Rae Cho
- Division of Software, Yonsei University Mirae Campus, Wonju-si 26493, Gangwon-do, Republic of Korea
- Division of Digital Healthcare, Yonsei University Mirae Campus, Wonju-si 26493, Gangwon-do, Republic of Korea
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20
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Shokri Garjan H, Omidi Y, Poursheikhali Asghari M, Ferdousi R. In-silico computational approaches to study microbiota impacts on diseases and pharmacotherapy. Gut Pathog 2023; 15:10. [PMID: 36882861 PMCID: PMC9990230 DOI: 10.1186/s13099-023-00535-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 02/21/2023] [Indexed: 03/09/2023] Open
Abstract
Microorganisms have been linked to a variety of critical human disease, thanks to advances in sequencing technology and microbiology. The growing recognition of human microbe-disease relationships provides crucial insights into the underlying disease process from the perspective of pathogens, which is extremely useful for pathogenesis research, early diagnosis, and precision medicine and therapy. Microbe-based analysis in terms of diseases and related drug discovery can predict new connections/mechanisms and provide new concepts. These phenomena have been studied via various in-silico computational approaches. This review aims to elaborate on the computational works conducted on the microbe-disease and microbe-drug topics, discuss the computational model approaches used for predicting associations and provide comprehensive information on the related databases. Finally, we discussed potential prospects and obstacles in this field of study, while also outlining some recommendations for further enhancing predictive capabilities.
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Affiliation(s)
- Hassan Shokri Garjan
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Yadollah Omidi
- Department of Pharmaceutical Sciences, Nova Southeastern University, College of Pharmacy, Fort Lauderdale, FL, USA
| | | | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
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21
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Yang X, Yang G, Chu J. The Computational Drug Repositioning Without Negative Sampling. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1506-1517. [PMID: 36197871 DOI: 10.1109/tcbb.2022.3212051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Computational drug repositioning technology is an effective tool to accelerate drug development. Although this technique has been widely used and successful in recent decades, many existing models still suffer from multiple drawbacks such as the massive number of unvalidated drug-disease associations and the inner product. The limitations of these works are mainly due to the following two reasons: firstly, previous works used negative sampling techniques to treat unvalidated drug-disease associations as negative samples, which is invalid in real-world settings; secondly, the inner product cannot fully take into account the feature information contained in the latent factor of drug and disease. In this paper, we propose a novel PUON framework for addressing the above deficiencies, which models the risk estimator of computational drug repositioning only using validated (Positive) and unvalidated (Unlabelled) drug-disease associations without employing negative sampling techniques. The PUON also proposed an Outer Neighborhood-based classifier for modeling the cross-feature information of the latent facotor. For a comprehensive comparison, we considered 6 popular baselines. Extensive experiments in four real-world datasets showed that PUON model achieved the best performance based on 6 evaluation metrics.
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22
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Azuma I, Mizuno T, Kusuhara H. NRBdMF: A Recommendation Algorithm for Predicting Drug Effects Considering Directionality. J Chem Inf Model 2023; 63:474-483. [PMID: 36635231 DOI: 10.1021/acs.jcim.2c01210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Predicting the novel effects of drugs based on information about approved drugs can be regarded as a recommendation system. Matrix factorization is one of the most used recommendation systems, and various algorithms have been devised for it. A literature survey and summary of existing algorithms for predicting drug effects demonstrated that most such methods, including neighborhood regularized logistic matrix factorization, which was the best performer in benchmark tests, used a binary matrix that considers only the presence or absence of interactions. However, drug effects are known to have two opposite aspects, such as side effects and therapeutic effects. In the present study, we proposed using neighborhood regularized bidirectional matrix factorization (NRBdMF) to predict drug effects by incorporating bidirectionality, which is a characteristic property of drug effects. We used this proposed method for predicting side effects using a matrix that considered the bidirectionality of drug effects, in which known side effects were assigned a positive (+1) label and known treatment effects were assigned a negative (-1) label. The NRBdMF model, which utilizes drug bidirectional information, achieved enrichment of side effects at the top and indications at the bottom of the prediction list. This first attempt to consider the bidirectional nature of drug effects using NRBdMF showed that it reduced false positives and produced a highly interpretable output.
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Affiliation(s)
- Iori Azuma
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Bunkyo-ku, Tokyo113-0033, Japan
| | - Tadahaya Mizuno
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Bunkyo-ku, Tokyo113-0033, Japan
| | - Hiroyuki Kusuhara
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Bunkyo-ku, Tokyo113-0033, Japan
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23
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Yang X, Yang G, Chu J. The Neural Metric Factorization for Computational Drug Repositioning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:731-741. [PMID: 35061591 DOI: 10.1109/tcbb.2022.3144429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Computational drug repositioning aims to discover new therapeutic diseases for marketed drugs and has the advantages of low cost, short development cycle, and high controllability compared to traditional drug development. The matrix factorization model has become the cornerstone technique for computational drug repositioning due to its ease of implementation and excellent scalability. However, the matrix factorization model uses the inner product operation to represent the association between drugs and diseases, which is lacking in expressive ability. Moreover, the degree of similarity of drugs or diseases could not be implied on their respective latent factor vectors, which is not satisfy the common sense of drug discovery. Therefore, a neural metric factorization model for computational drug repositioning (NMFDR) is proposed in this work. We novelly consider the latent factor vector of drugs and diseases as a point in the high-dimensional coordinate system and propose a generalized euclidean distance to represent the association between drugs and diseases to compensate for the shortcomings of the inner product operation. Furthermore, by embedding multiple drug (disease) metrics information into the encoding space of the latent factor vector, the information about the similarity between drugs (diseases) can be reflected in the distance between latent factor vectors. Finally, we conduct wide analysis experiments on three real datasets to demonstrate the effectiveness of the above improvement points and the superiority of the NMFDR model.
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Wang Y, Xiang J, Liu C, Tang M, Hou R, Bao M, Tian G, He J, He B. Drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization. Front Microbiol 2022; 13:1062281. [PMID: 36545200 PMCID: PMC9762482 DOI: 10.3389/fmicb.2022.1062281] [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: 10/05/2022] [Accepted: 11/21/2022] [Indexed: 12/12/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19), a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently spreading rapidly around the world. Since SARS-CoV-2 seriously threatens human life and health as well as the development of the world economy, it is very urgent to identify effective drugs against this virus. However, traditional methods to develop new drugs are costly and time-consuming, which makes drug repositioning a promising exploration direction for this purpose. In this study, we collected known antiviral drugs to form five virus-drug association datasets, and then explored drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization (VDA-GKSBMF). By the 5-fold cross-validation, we found that VDA-GKSBMF has an area under curve (AUC) value of 0.8851, 0.8594, 0.8807, 0.8824, and 0.8804, respectively, on the five datasets, which are higher than those of other state-of-art algorithms in four datasets. Based on known virus-drug association data, we used VDA-GKSBMF to prioritize the top-k candidate antiviral drugs that are most likely to be effective against SARS-CoV-2. We confirmed that the top-10 drugs can be molecularly docked with virus spikes protein/human ACE2 by AutoDock on five datasets. Among them, four antiviral drugs ribavirin, remdesivir, oseltamivir, and zidovudine have been under clinical trials or supported in recent literatures. The results suggest that VDA-GKSBMF is an effective algorithm for identifying potential antiviral drugs against SARS-CoV-2.
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Affiliation(s)
- Yibai Wang
- School of Information Engineering, Changsha Medical University, Changsha, China
| | - Ju Xiang
- School of Information Engineering, Changsha Medical University, Changsha, China,Academician Workstation, Changsha Medical University, Changsha, China,*Correspondence: Ju Xiang,
| | - Cuicui Liu
- School of Information Engineering, Changsha Medical University, Changsha, China
| | - Min Tang
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Rui Hou
- Geneis (Beijing) Co., Ltd., Beijing, China,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Meihua Bao
- School of Pharmacy, Changsha Medical University, Changsha, China,Key Laboratory Breeding Base of Hunan Oriented Fundamental and Applied Research of Innovative Pharmaceutics, Changsha Medical University, Changsha, China
| | - Geng Tian
- Geneis (Beijing) Co., Ltd., Beijing, China,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jianjun He
- Academician Workstation, Changsha Medical University, Changsha, China,School of Pharmacy, Changsha Medical University, Changsha, China,Key Laboratory Breeding Base of Hunan Oriented Fundamental and Applied Research of Innovative Pharmaceutics, Changsha Medical University, Changsha, China,Jianjun He,
| | - Binsheng He
- Academician Workstation, Changsha Medical University, Changsha, China,School of Pharmacy, Changsha Medical University, Changsha, China,Key Laboratory Breeding Base of Hunan Oriented Fundamental and Applied Research of Innovative Pharmaceutics, Changsha Medical University, Changsha, China,Binsheng He,
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Yang J, Li Z, Wu WKK, Yu S, Xu Z, Chu Q, Zhang Q. Deep learning identifies explainable reasoning paths of mechanism of action for drug repurposing from multilayer biological network. Brief Bioinform 2022; 23:6809964. [PMID: 36347526 DOI: 10.1093/bib/bbac469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 09/07/2022] [Accepted: 09/29/2022] [Indexed: 11/11/2022] Open
Abstract
The discovery and repurposing of drugs require a deep understanding of the mechanism of drug action (MODA). Existing computational methods mainly model MODA with the protein-protein interaction (PPI) network. However, the molecular interactions of drugs in the human body are far beyond PPIs. Additionally, the lack of interpretability of these models hinders their practicability. We propose an interpretable deep learning-based path-reasoning framework (iDPath) for drug discovery and repurposing by capturing MODA on by far the most comprehensive multilayer biological network consisting of the complex high-dimensional molecular interactions between genes, proteins and chemicals. Experiments show that iDPath outperforms state-of-the-art machine learning methods on a general drug repurposing task. Further investigations demonstrate that iDPath can identify explicit critical paths that are consistent with clinical evidence. To demonstrate the practical value of iDPath, we apply it to the identification of potential drugs for treating prostate cancer and hypertension. Results show that iDPath can discover new FDA-approved drugs. This research provides a novel interpretable artificial intelligence perspective on drug discovery.
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Affiliation(s)
- Jiannan Yang
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - William Ka Kei Wu
- Department of Anaesthesia and Intensive Care, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Shi Yu
- The USC Norris Center for Cancer Drug Development, University of Southern California, Los Angeles, CA, USA.,Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zhongzhi Xu
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
| | - Qian Chu
- Department of Thoracic Oncology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Qingpeng Zhang
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
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Mongia A, Chouzenoux E, Majumdar A. Computational Prediction of Drug-Disease Association Based on Graph-Regularized One Bit Matrix Completion. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3332-3339. [PMID: 35816539 DOI: 10.1109/tcbb.2022.3189879] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Investigation of existing drugs is an effective alternative to the discovery of new drugs for treating diseases. This task of drug re-positioning can be assisted by various kinds of computational methods to predict the best indication for a drug given the open-source biological datasets. Owing to the fact that similar drugs tend to have common pathways and disease indications, the association matrix is assumed to be of low-rank structure. Hence, the problem of drug-disease association prediction can be modeled as a low-rank matrix completion problem. In this work, we propose a novel matrix completion framework that makes use of the side-information associated with drugs/diseases for the prediction of drug-disease indications modeled as neighborhood graph: Graph regularized 1-bit matrix completion (GR1BMC). The algorithm is specially designed for binary data and uses parallel proximal algorithm to solve the aforesaid minimization problem taking into account all the constraints including the neighborhood graph incorporation and restricting predicted scores within the specified range. The results have been validated on two standard databases by evaluating the AUC across the 10-fold cross-validation splits. The usage of the method is also evaluated through a case study where top 5 indications are predicted for novel drugs, which then are verified with the CTD database.
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Gu Y, Zheng S, Yin Q, Jiang R, Li J. REDDA: Integrating multiple biological relations to heterogeneous graph neural network for drug-disease association prediction. Comput Biol Med 2022; 150:106127. [PMID: 36182762 DOI: 10.1016/j.compbiomed.2022.106127] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/27/2022] [Accepted: 09/18/2022] [Indexed: 11/03/2022]
Abstract
Computational drug repositioning is an effective way to find new indications for existing drugs, thus can accelerate drug development and reduce experimental costs. Recently, various deep learning-based repurposing methods have been established to identify the potential drug-disease associations (DDA). However, effective utilization of the relations of biological entities to capture the biological interactions to enhance the drug-disease association prediction is still challenging. To resolve the above problem, we proposed a heterogeneous graph neural network called REDDA (Relations-Enhanced Drug-Disease Association prediction). Assembled with three attention mechanisms, REDDA can sequentially learn drug/disease representations by a general heterogeneous graph convolutional network-based node embedding block, a topological subnet embedding block, a graph attention block, and a layer attention block. Performance comparisons on our proposed benchmark dataset show that REDDA outperforms 8 advanced drug-disease association prediction methods, achieving relative improvements of 0.76% on the area under the receiver operating characteristic curve (AUC) score and 13.92% on the precision-recall curve (AUPR) score compared to the suboptimal method. On the other benchmark dataset, REDDA also obtains relative improvements of 2.48% on the AUC score and 4.93% on the AUPR score. Specifically, case studies also indicate that REDDA can give valid predictions for the discovery of -new indications for drugs and new therapies for diseases. The overall results provide an inspiring potential for REDDA in the in silico drug development. The proposed benchmark dataset and source code are available in https://github.com/gu-yaowen/REDDA.
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Affiliation(s)
- Yaowen Gu
- Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, 100020, China
| | - Si Zheng
- Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, 100020, China; Institute for Artificial Intelligence, Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, 100084, China
| | - Qijin Yin
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Jiao Li
- Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, 100020, China.
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28
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Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning. Biomolecules 2022; 12:biom12101497. [PMID: 36291706 PMCID: PMC9599692 DOI: 10.3390/biom12101497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/10/2022] [Accepted: 10/13/2022] [Indexed: 11/18/2022] Open
Abstract
Drug repositioning, which involves the identification of new therapeutic indications for approved drugs, considerably reduces the time and cost of developing new drugs. Recent computational drug repositioning methods use heterogeneous networks to identify drug–disease associations. This review reveals existing network-based approaches for predicting drug–disease associations in three major categories: graph mining, matrix factorization or completion, and deep learning. We selected eleven methods from the three categories to compare their predictive performances. The experiment was conducted using two uniform datasets on the drug and disease sides, separately. We constructed heterogeneous networks using drug–drug similarities based on chemical structures and ATC codes, ontology-based disease–disease similarities, and drug–disease associations. An improved evaluation metric was used to reflect data imbalance as positive associations are typically sparse. The prediction results demonstrated that methods in the graph mining and matrix factorization or completion categories performed well in the overall assessment. Furthermore, prediction on the drug side had higher accuracy than on the disease side. Selecting and integrating informative drug features in drug–drug similarity measurement are crucial for improving disease-side prediction.
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29
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Lian M, Wang X, Du W. Integrated multi-similarity fusion and heterogeneous graph inference for drug-target interaction prediction. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.04.104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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30
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Rintala TJ, Ghosh A, Fortino V. Network approaches for modeling the effect of drugs and diseases. Brief Bioinform 2022; 23:6608969. [PMID: 35704883 PMCID: PMC9294412 DOI: 10.1093/bib/bbac229] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/29/2022] [Accepted: 05/17/2021] [Indexed: 12/12/2022] Open
Abstract
The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug’s MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for the treatment of diseases. We also discuss recent studies using biological and omics-driven networks to search for possible repurposed FDA-approved drug treatments for SARS-CoV-2 infections (COVID-19).
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Affiliation(s)
- T J Rintala
- Institute of Biomedicine, University of Eastern Finland, 70210 Kuopio, Finland
| | - Arindam Ghosh
- Institute of Biomedicine, University of Eastern Finland, 70210 Kuopio, Finland
| | - V Fortino
- Institute of Biomedicine, University of Eastern Finland, 70210 Kuopio, Finland
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31
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Zhu S, Bai Q, Li L, Xu T. Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents. Comput Struct Biotechnol J 2022; 20:2839-2847. [PMID: 35765655 PMCID: PMC9189996 DOI: 10.1016/j.csbj.2022.05.057] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/30/2022] [Accepted: 05/30/2022] [Indexed: 12/19/2022] Open
Abstract
Repositioning or repurposing drugs account for a substantial part of entering approval pipeline drugs, which indicates that drug repositioning has huge market potential and value. Computational technologies such as machine learning methods have accelerated the process of drug repositioning in the last few decades years. The repositioning potential of type 2 diabetes mellitus (T2DM) drugs for various diseases such as cancer, neurodegenerative diseases, and cardiovascular diseases have been widely studied. Hence, the related summary about repurposing antidiabetic drugs is of great significance. In this review, we focus on the machine learning methods for the development of new T2DM drugs and give an overview of the repurposing potential of the existing antidiabetic agents.
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Affiliation(s)
- Sha Zhu
- Key Lab of Preclinical Study for New Drugs of Gansu Province, Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu 730000, PR China
| | - Qifeng Bai
- Key Lab of Preclinical Study for New Drugs of Gansu Province, Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu 730000, PR China
- Corresponding author.
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32
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Xu J, Meng Y, Peng L, Cai L, Tang X, Liang Y, Tian G, Yang J. Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID-19. J Cell Mol Med 2022; 26:3772-3782. [PMID: 35644992 PMCID: PMC9258716 DOI: 10.1111/jcmm.17412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 02/03/2022] [Accepted: 05/11/2022] [Indexed: 02/06/2023] Open
Abstract
Amid the COVID‐19 crisis, we put sizeable efforts to collect a high number of experimentally validated drug–virus association entries from literature by text mining and built a human drug–virus association database. To the best of our knowledge, it is the largest publicly available drug–virus database so far. Next, we develop a novel weight regularization matrix factorization approach, termed WRMF, for in silico drug repurposing by integrating three networks: the known drug–virus association network, the drug–drug chemical structure similarity network, and the virus–virus genomic sequencing similarity network. Specifically, WRMF adds a weight to each training sample for reducing the influence of negative samples (i.e. the drug–virus association is unassociated). A comparison on the curated drug–virus database shows that WRMF performs better than a few state‐of‐the‐art methods. In addition, we selected the other two different public datasets (i.e. Cdataset and HMDD V2.0) to assess WRMF's performance. The case study also demonstrated the accuracy and reliability of WRMF to infer potential drugs for the novel virus. In summary, we offer a useful tool including a novel drug–virus association database and a powerful method WRMF to repurpose potential drugs for new viruses.
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Affiliation(s)
- Junlin Xu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Yajie Meng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Lijun Cai
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Xianfang Tang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | | | - Geng Tian
- Geneis Beijing Co., Ltd., Beijing, China
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33
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Xu A, Wen ZH, Su SX, Chen YP, Liu WC, Guo SQ, Li XF, Zhang X, Li R, Xu NB, Wang KX, Li WX, Guan DG, Duan CZ. Elucidating the Synergistic Effect of Multiple Chinese Herbal Prescriptions in the Treatment of Post-stroke Neurological Damage. Front Pharmacol 2022; 13:784242. [PMID: 35355727 PMCID: PMC8959705 DOI: 10.3389/fphar.2022.784242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 02/14/2022] [Indexed: 12/12/2022] Open
Abstract
Background: Traditional Chinese medicine (TCM) has been widely used in the treatment of human diseases. However, the synergistic effects of multiple TCM prescriptions in the treatment of stroke have not been thoroughly studied. Objective of the study: This study aimed to reveal the mechanisms underlying the synergistic effects of these TCM prescriptions in stroke treatment and identify the active compounds. Methods: Herbs and compounds in the Di-Tan Decoction (DTD), Xue-Fu Zhu-Yu Decoction (XFZYD), and Xiao-Xu-Ming Decoction (XXMD) were acquired from the TCMSP database. SEA, HitPick, and TargetNet web servers were used for target prediction. The compound-target (C-T) networks of three prescriptions were constructed and then filtered using the collaborative filtering algorithm. We combined KEGG enrichment analysis, molecular docking, and network analysis approaches to identify active compounds, followed by verification of these compounds with an oxygen-glucose deprivation and reoxygenation (OGD/R) model. Results: The filtered DTD network contained 39 compounds and 534 targets, the filtered XFZYD network contained 40 compounds and 508 targets, and the filtered XXMD network contained 55 compounds and 599 targets. The filtered C-T networks retained approximately 80% of the biological functions of the original networks. Based on the enriched pathways, molecular docking, and network analysis results, we constructed a complex network containing 3 prescriptions, 14 botanical drugs, 26 compounds, 13 targets, and 5 pathways. By calculating the synergy score, we identified the top 5 candidate compounds. The experimental results showed that quercetin, baicalin, and ginsenoside Rg1 independently and synergistically increased cell viability. Conclusion: By integrating pharmacological and chemoinformatic approaches, our study provides a new method for identifying the effective synergistic compounds of TCM prescriptions. The filtered compounds and their synergistic effects on stroke require further research.
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Affiliation(s)
- Anqi Xu
- Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, National Key Clinical Specialty/Engineering Technology Research Center of Education Ministry of China, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zhuo-Hua Wen
- Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, National Key Clinical Specialty/Engineering Technology Research Center of Education Ministry of China, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Shi-Xing Su
- Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, National Key Clinical Specialty/Engineering Technology Research Center of Education Ministry of China, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yu-Peng Chen
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Wen-Chao Liu
- Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, National Key Clinical Specialty/Engineering Technology Research Center of Education Ministry of China, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Shen-Quan Guo
- Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, National Key Clinical Specialty/Engineering Technology Research Center of Education Ministry of China, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xi-Feng Li
- Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, National Key Clinical Specialty/Engineering Technology Research Center of Education Ministry of China, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xin Zhang
- Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, National Key Clinical Specialty/Engineering Technology Research Center of Education Ministry of China, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Ran Li
- Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, National Key Clinical Specialty/Engineering Technology Research Center of Education Ministry of China, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Ning-Bo Xu
- Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, National Key Clinical Specialty/Engineering Technology Research Center of Education Ministry of China, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Ke-Xin Wang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Wen-Xing Li
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Dao-Gang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Chuan-Zhi Duan
- Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, National Key Clinical Specialty/Engineering Technology Research Center of Education Ministry of China, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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34
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Li X, Shong K, Kim W, Yuan M, Yang H, Sato Y, Kume H, Ogawa S, Turkez H, Shoaie S, Boren J, Nielsen J, Uhlen M, Zhang C, Mardinoglu A. Prediction of drug candidates for clear cell renal cell carcinoma using a systems biology-based drug repositioning approach. EBioMedicine 2022; 78:103963. [PMID: 35339898 PMCID: PMC8960981 DOI: 10.1016/j.ebiom.2022.103963] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 03/09/2022] [Accepted: 03/09/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The response rates of the clinical chemotherapies are still low in clear cell renal cell carcinoma (ccRCC). Computational drug repositioning is a promising strategy to discover new uses for existing drugs to treat patients who cannot get benefits from clinical drugs. METHODS We proposed a systematic approach which included the target prediction based on the co-expression network analysis of transcriptomics profiles of ccRCC patients and drug repositioning for cancer treatment based on the analysis of shRNA- and drug-perturbed signature profiles of human kidney cell line. FINDINGS First, based on the gene co-expression network analysis, we identified two types of gene modules in ccRCC, which significantly enriched with unfavorable and favorable signatures indicating poor and good survival outcomes of patients, respectively. Then, we selected four genes, BUB1B, RRM2, ASF1B and CCNB2, as the potential drug targets based on the topology analysis of modules. Further, we repurposed three most effective drugs for each target by applying the proposed drug repositioning approach. Finally, we evaluated the effects of repurposed drugs using an in vitro model and observed that these drugs inhibited the protein levels of their corresponding target genes and cell viability. INTERPRETATION These findings proved the usefulness and efficiency of our approach to improve the drug repositioning researches for cancer treatment and precision medicine. FUNDING This study was funded by Knut and Alice Wallenberg Foundation and Bash Biotech Inc., San Diego, CA, USA.
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Affiliation(s)
- Xiangyu Li
- Bash Biotech Inc, 600 est Broadway, Suite 700, San Diego, CA 92101, USA; Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Koeun Shong
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Woonghee Kim
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Meng Yuan
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Hong Yang
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Yusuke Sato
- Department of Pathology and Tumor Biology, Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto 606-8501, Japan; Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan
| | - Haruki Kume
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto 606-8501, Japan; Centre for Hematology and Regenerative Medicine, Department of Medicine, Karolinska Institute, Stockholm SE-17177, Sweden
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum 25240, Turkey
| | - Saeed Shoaie
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg SE-41345, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-41296, Sweden; BioInnovation Institute, Copenhagen N DK-2200, Denmark
| | - Mathias Uhlen
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden; Key Laboratory of Advanced Drug Preparation Technologies, School of Pharmaceutical Sciences, Ministry of Education, Zhengzhou University, Zhengzhou 450001, China.
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK.
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35
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Wang L, Tan Y, Yang X, Kuang L, Ping P. Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models. Brief Bioinform 2022; 23:6553604. [PMID: 35325024 DOI: 10.1093/bib/bbac080] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 12/11/2022] Open
Abstract
In recent years, with the rapid development of techniques in bioinformatics and life science, a considerable quantity of biomedical data has been accumulated, based on which researchers have developed various computational approaches to discover potential associations between human microbes, drugs and diseases. This paper provides a comprehensive overview of recent advances in prediction of potential correlations between microbes, drugs and diseases from biological data to computational models. Firstly, we introduced the widely used datasets relevant to the identification of potential relationships between microbes, drugs and diseases in detail. And then, we divided a series of a lot of representative computing models into five major categories including network, matrix factorization, matrix completion, regularization and artificial neural network for in-depth discussion and comparison. Finally, we analysed possible challenges and opportunities in this research area, and at the same time we outlined some suggestions for further improvement of predictive performances as well.
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Affiliation(s)
- Lei Wang
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Yaqin Tan
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Xiaoyu Yang
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Linai Kuang
- Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Pengyao Ping
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China
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36
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Yan Y, Yang M, Zhao H, Duan G, Peng X, Wang J. Drug repositioning based on multi-view learning with matrix completion. Brief Bioinform 2022; 23:6548374. [PMID: 35289352 DOI: 10.1093/bib/bbac054] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/14/2022] [Accepted: 01/31/2022] [Indexed: 12/21/2022] Open
Abstract
Determining drug indications is a critical part of the drug development process. However, traditional drug discovery is expensive and time-consuming. Drug repositioning aims to find potential indications for existing drugs, which is considered as an important alternative to the traditional drug discovery. In this article, we propose a multi-view learning with matrix completion (MLMC) method to predict the potential associations between drugs and diseases. Specifically, MLMC first learns the comprehensive similarity matrices from five drug similarity matrices and two disease similarity matrices based on the multi-view learning (ML) with Laplacian graph regularization, and updates the drug-disease association matrix simultaneously. Then, we introduce matrix completion (MC) to add some positive entries in original association matrix based on low-rank structure, and re-execute the multi-view learning algorithm for association prediction. At last, the prediction results of the above two operations are integrated as the final output. Evaluated by 10-fold cross-validation and de novo tests, MLMC achieves higher prediction accuracy than the current state-of-the-art methods. Moreover, case studies confirm the ability of our method in novel drug-disease association discovery. The codes of MLMC are available at https://github.com/BioinformaticsCSU/MLMC. Contact: jxwang@mail.csu.edu.cn.
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Affiliation(s)
- Yixin Yan
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Mengyun Yang
- Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang, Hunan 422000, China
| | - Haochen Zhao
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Guihua Duan
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Xiaoqing Peng
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410038, China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
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37
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Sun J, Lu Y, Cui L, Fu Q, Wu H, Chen J. A Method of Optimizing Weight Allocation in Data Integration Based on Q-Learning for Drug-Target Interaction Prediction. Front Cell Dev Biol 2022; 10:794413. [PMID: 35356288 PMCID: PMC8959213 DOI: 10.3389/fcell.2022.794413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 02/14/2022] [Indexed: 11/26/2022] Open
Abstract
Calculating and predicting drug-target interactions (DTIs) is a crucial step in the field of novel drug discovery. Nowadays, many models have improved the prediction performance of DTIs by fusing heterogeneous information, such as drug chemical structure and target protein sequence and so on. However, in the process of fusion, how to allocate the weight of heterogeneous information reasonably is a huge challenge. In this paper, we propose a model based on Q-learning algorithm and Neighborhood Regularized Logistic Matrix Factorization (QLNRLMF) to predict DTIs. First, we obtain three different drug-drug similarity matrices and three different target-target similarity matrices by using different similarity calculation methods based on heterogeneous data, including drug chemical structure, target protein sequence and drug-target interactions. Then, we initialize a set of weights for the drug-drug similarity matrices and target-target similarity matrices respectively, and optimize them through Q-learning algorithm. When the optimal weights are obtained, a new drug-drug similarity matrix and a new drug-drug similarity matrix are obtained by linear combination. Finally, the drug target interaction matrix, the new drug-drug similarity matrices and the target-target similarity matrices are used as inputs to the Neighborhood Regularized Logistic Matrix Factorization (NRLMF) model for DTIs. Compared with the existing six methods of NetLapRLS, BLM-NII, WNN-GIP, KBMF2K, CMF, and NRLMF, our proposed method has achieved better effect in the four benchmark datasets, including enzymes(E), nuclear receptors (NR), ion channels (IC) and G protein coupled receptors (GPCR).
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Affiliation(s)
- Jiacheng Sun
- School of Electronic and Information Engineering, SuZhou University of Science and Technology, Suzhou, China
- Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, China
- Suzhou Key Laboratory of Mobile Network Technology and Application, Suzhou University of Science and Technology, Suzhou, China
| | - You Lu
- School of Electronic and Information Engineering, SuZhou University of Science and Technology, Suzhou, China
- Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, China
- Suzhou Key Laboratory of Mobile Network Technology and Application, Suzhou University of Science and Technology, Suzhou, China
- *Correspondence: You Lu, ; Jianping Chen,
| | - Linqian Cui
- School of Electronic and Information Engineering, SuZhou University of Science and Technology, Suzhou, China
- Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, China
- Suzhou Key Laboratory of Mobile Network Technology and Application, Suzhou University of Science and Technology, Suzhou, China
| | - Qiming Fu
- School of Electronic and Information Engineering, SuZhou University of Science and Technology, Suzhou, China
- Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, China
- Suzhou Key Laboratory of Mobile Network Technology and Application, Suzhou University of Science and Technology, Suzhou, China
| | - Hongjie Wu
- School of Electronic and Information Engineering, SuZhou University of Science and Technology, Suzhou, China
| | - Jianping Chen
- Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, China
- School of Architecture and Urban Planning, Suzhou University of Science and Technology, Suzhou, China
- *Correspondence: You Lu, ; Jianping Chen,
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38
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Meng Y, Lu C, Jin M, Xu J, Zeng X, Yang J. A weighted bilinear neural collaborative filtering approach for drug repositioning. Brief Bioinform 2022; 23:6510159. [PMID: 35039838 DOI: 10.1093/bib/bbab581] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/25/2021] [Accepted: 12/19/2021] [Indexed: 02/07/2023] Open
Abstract
Drug repositioning is an efficient and promising strategy for traditional drug discovery and development. Many research efforts are focused on utilizing deep-learning approaches based on a heterogeneous network for modeling complex drug-disease associations. Similar to traditional latent factor models, which directly factorize drug-disease associations, they assume the neighbors are independent of each other in the network and thus tend to be ineffective to capture localized information. In this study, we propose a novel neighborhood and neighborhood interaction-based neural collaborative filtering approach (called DRWBNCF) to infer novel potential drugs for diseases. Specifically, we first construct three networks, including the known drug-disease association network, the drug-drug similarity and disease-disease similarity networks (using the nearest neighbors). To take the advantage of localized information in the three networks, we then design an integration component by proposing a new weighted bilinear graph convolution operation to integrate the information of the known drug-disease association, the drug's and disease's neighborhood and neighborhood interactions into a unified representation. Lastly, we introduce a prediction component, which utilizes the multi-layer perceptron optimized by the α-balanced focal loss function and graph regularization to model the complex drug-disease associations. Benchmarking comparisons on three datasets verified the effectiveness of DRWBNCF for drug repositioning. Importantly, the unknown drug-disease associations predicted by DRWBNCF were validated against clinical trials and three authoritative databases and we listed several new DRWBNCF-predicted potential drugs for breast cancer (e.g. valrubicin and teniposide) and small cell lung cancer (e.g. valrubicin and cytarabine).
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Affiliation(s)
- Yajie Meng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Changcheng Lu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Min Jin
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Junlin Xu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
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39
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Huang S, Ding Y. Identification of Anticancer and Anti-inflammatory Drugs from Drug-target Interaction Descriptors by Machine Learning.. LETT DRUG DES DISCOV 2022. [DOI: 10.2174/1570180819666220114114752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Drug repositioning is an important subject in drug-disease research. In the past, most studies simply used drug descriptors as the feature vector to classify drugs or targets, or used qualitative data about drug-target or drug-disease to predict drug-target interactions. These data provide limited information for drug repositioning.
Objective:
Considering both drugs and targets and constructing quantitative drug-target interaction descriptors as a method of drug characteristics are of great significance to the study of drug repositioning.
Methods:
Taking anticancer and anti-inflammatory drugs as research objects, the interaction sites between drugs and targets were determined by molecular docking. Sixty-seven drug-target interaction descriptors were calculated to describe the drug-target interactions, and 22 important descriptors were screened for drug classification by SVM, LightGBM and MLP.
Results:
The accuracy of SVM, LightGBM and MLP reached 93.29%, 92.68% and 94.51%, their Matthews correlation coefficients reached 0.852, 0.840 and 0.882, and their areas under the ROC curve reached 0.977, 0.969 and 0.968, respectively.
Conclusion:
Using drug-target interaction descriptors to build machine learning models can obtain better results for drug classification. Number of atom pairs, force field, hydrophobic interactions and bSASA are the four types of key features for the classification of anticancer and anti-inflammatory drugs.
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Affiliation(s)
- Songtao Huang
- School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, P.R. China
- Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi, Jiangsu, 214122, P.R. China
| | - Yanrui Ding
- school of Science, Jiangnan University, Wuxi, Jiangsu, 214122, P.R. China
- Key Laboratory of Industrial Biotechnology, Jiangnan University, Wuxi, Jiangsu, 214122, P.R. China
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40
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Sadeghi S, Lu J, Ngom A. A network-based drug repurposing method via non-negative matrix factorization. Bioinformatics 2021; 38:1369-1377. [PMID: 34875000 PMCID: PMC8825773 DOI: 10.1093/bioinformatics/btab826] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 11/05/2021] [Accepted: 12/01/2021] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Drug repurposing is a potential alternative to the traditional drug discovery process. Drug repurposing can be formulated as a recommender system that recommends novel indications for available drugs based on known drug-disease associations. This article presents a method based on non-negative matrix factorization (NMF-DR) to predict the drug-related candidate disease indications. This work proposes a recommender system-based method for drug repurposing to predict novel drug indications by integrating drug and diseases related data sources. For this purpose, this framework first integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different views to make a heterogeneous drug-disease interaction network. Then, an improved non-negative matrix factorization-based method is proposed to complete the drug-disease adjacency matrix with predicted scores for unknown drug-disease pairs. RESULTS The comprehensive experimental results show that NMF-DR achieves superior prediction performance when compared with several existing methods for drug-disease association prediction. AVAILABILITY AND IMPLEMENTATION The program is available at https://github.com/sshaghayeghs/NMF-DR. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shaghayegh Sadeghi
- School of Computer Science, University of Windsor, 401 Sunset Avenue, N9B 3P4, Windsor, Ontario, Canada,To whom correspondence should be addressed.
| | - Jianguo Lu
- School of Computer Science, University of Windsor, 401 Sunset Avenue, N9B 3P4, Windsor, Ontario, Canada
| | - Alioune Ngom
- School of Computer Science, University of Windsor, 401 Sunset Avenue, N9B 3P4, Windsor, Ontario, Canada
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41
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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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Yang X, Kuang L, Chen Z, Wang L. Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe-Disease Associations. Front Genet 2021; 12:754425. [PMID: 34721543 PMCID: PMC8551558 DOI: 10.3389/fgene.2021.754425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 09/21/2021] [Indexed: 11/13/2022] Open
Abstract
Accumulating studies have shown that microbes are closely related to human diseases. In this paper, a novel method called MSBMFHMDA was designed to predict potential microbe-disease associations by adopting multi-similarities bilinear matrix factorization. In MSBMFHMDA, a microbe multiple similarities matrix was constructed first based on the Gaussian interaction profile kernel similarity and cosine similarity for microbes. Then, we use the Gaussian interaction profile kernel similarity, cosine similarity, and symptom similarity for diseases to compose the disease multiple similarities matrix. Finally, we integrate these two similarity matrices and the microbe-disease association matrix into our model to predict potential associations. The results indicate that our method can achieve reliable AUCs of 0.9186 and 0.9043 ± 0.0048 in the framework of leave-one-out cross validation (LOOCV) and fivefold cross validation, respectively. What is more, experimental results indicated that there are 10, 10, and 8 out of the top 10 related microbes for asthma, inflammatory bowel disease, and type 2 diabetes mellitus, respectively, which were confirmed by experiments and literatures. Therefore, our model has favorable performance in predicting potential microbe-disease associations.
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Affiliation(s)
- Xiaoyu Yang
- Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China.,College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Linai Kuang
- Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China
| | - Zhiping Chen
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Lei Wang
- Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China.,College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
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