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Sheng N, Xie X, Wang Y, Huang L, Zhang S, Gao L, Wang H. A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:328-347. [PMID: 38194377 DOI: 10.1109/tcbb.2024.3351752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pathogenesis. Traditional laboratory experiments are expensive and time-consuming. Computational models have enabled systematic large-scale prediction of potential MDAs, greatly improving the research efficiency. With recent advances in deep learning, it has become an attractive and powerful technique for uncovering novel MDAs. Consequently, numerous MDA prediction methods based on deep learning have emerged. In this review, we first summarize publicly available databases related to miRNAs and diseases for MDA prediction. Next, we outline commonly used miRNA and disease similarity calculation and integration methods. Then, we comprehensively review the 48 existing deep learning-based MDA computation methods, categorizing them into classical deep learning and graph neural network-based techniques. Subsequently, we investigate the evaluation methods and metrics that are frequently used to assess MDA prediction performance. Finally, we discuss the performance trends of different computational methods, point out some problems in current research, and propose 9 potential future research directions. Data resources and recent advances in MDA prediction methods are summarized in the GitHub repository https://github.com/sheng-n/DL-miRNA-disease-association-methods.
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Xu T, Gao W, Zhu L, Chen W, Niu C, Yin W, Ma L, Zhu X, Ling Y, Gao S, Liu L, Jiao N, Chen W, Zhang G, Zhu R, Wu D. NAFLDkb: A Knowledge Base and Platform for Drug Development against Nonalcoholic Fatty Liver Disease. J Chem Inf Model 2024; 64:2817-2828. [PMID: 37167092 DOI: 10.1021/acs.jcim.3c00395] [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: 05/13/2023]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease with a broad spectrum of histologic manifestations. The rapidly growing prevalence and the complex pathologic mechanisms of NAFLD pose great challenges for treatment development. Despite tremendous efforts devoted to drug development, there are no FDA-approved medicines yet. Here, we present NAFLDkb, a specialized knowledge base and platform for computer-aided drug design against NAFLD. With multiperspective information curated from diverse source materials and public databases, NAFLDkb presents the associations of drug-related entities as individual knowledge graphs. Practical drug discovery tools that facilitate the utilization and expansion of NAFLDkb have also been implemented in the web interface, including chemical structure search, drug-likeness screening, knowledge-based repositioning, and research article annotation. Moreover, case studies of a knowledge graph repositioning model and a generative neural network model are presented herein, where three repositioning drug candidates and 137 novel lead-like compounds were newly established as NAFLD pharmacotherapy options reusing data records and machine learning tools in NAFLDkb, suggesting its clinical reliability and great potential in identifying novel drug-disease associations of NAFLD and generating new insights to accelerate NAFLD drug development. NAFLDkb is freely accessible at https://www.biosino.org/nafldkb and will be updated periodically with the latest findings.
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Affiliation(s)
- Tingjun Xu
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
- Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 LingLing Road, Shanghai 200032, P. R. China
| | - Wenxing Gao
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Lixin Zhu
- Guangdong Institute of Gastroenterology; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases; Biomedical Innovation Center, Sun Yat-sen University, Guangzhou 510655, P. R. China
- Department of General Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou 510655, P. R. China
| | - Wanning Chen
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Chaoqun Niu
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. China
| | - Wenjing Yin
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Liangxiao Ma
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. China
| | - Xinyue Zhu
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Yunchao Ling
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. China
| | - Sheng Gao
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Lei Liu
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Na Jiao
- National Clinical Research Center for Child Health, the Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, P. R. China
| | - Weiming Chen
- Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 LingLing Road, Shanghai 200032, P. R. China
| | - Guoqing Zhang
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. China
| | - Ruixin Zhu
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Dingfeng Wu
- National Clinical Research Center for Child Health, the Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, P. R. China
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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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Sheng N, Huang L, Gao L, Cao Y, Xie X, Wang Y. A Survey of Computational Methods and Databases for lncRNA-MiRNA Interaction Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2810-2826. [PMID: 37030713 DOI: 10.1109/tcbb.2023.3264254] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two prevalent non-coding RNAs in current research. They play critical regulatory roles in the life processes of animals and plants. Studies have shown that lncRNAs can interact with miRNAs to participate in post-transcriptional regulatory processes, mainly involved in regulating cancer development, metastatic progression, and drug resistance. Additionally, these interactions have significant effects on plant growth, development, and responses to biotic and abiotic stresses. Deciphering the potential relationships between lncRNAs and miRNAs may provide new insights into our understanding of the biological functions of lncRNAs and miRNAs, and the pathogenesis of complex diseases. In contrast, gathering information on lncRNA-miRNA interactions (LMIs) through biological experiments is expensive and time-consuming. With the accumulation of multi-omics data, computational models are extremely attractive in systematically exploring potential LMIs. To the best of our knowledge, this is the first comprehensive review of computational methods for identifying LMIs. Specifically, we first summarized the available public databases for predicting animal and plant LMIs. Second, we comprehensively reviewed the computational methods for predicting LMIs and classified them into two categories, including network-based methods and sequence-based methods. Third, we analyzed the standard evaluation methods and metrics used in LMI prediction. Finally, we pointed out some problems in the current study and discuss future research directions. Relevant databases and the latest advances in LMI prediction are summarized in a GitHub repository https://github.com/sheng-n/lncRNA-miRNA-interaction-methods, and we'll keep it updated.
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Sinha K, Ghosh N, Sil PC. A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies. Chem Res Toxicol 2023; 36:1174-1205. [PMID: 37561655 DOI: 10.1021/acs.chemrestox.2c00375] [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: 08/12/2023]
Abstract
Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.
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Affiliation(s)
- Krishnendu Sinha
- Department of Zoology, Jhargram Raj College, Jhargram 721507, West Bengal, India
| | - Nabanita Ghosh
- Department of Zoology, Maulana Azad College, Kolkata 700013, West Bengal, India
| | - Parames C Sil
- Division of Molecular Medicine, Bose Institute, Kolkata 700054, West Bengal, India
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Sheng N, Huang L, Lu Y, Wang H, Yang L, Gao L, Xie X, Fu Y, Wang Y. Data resources and computational methods for lncRNA-disease association prediction. Comput Biol Med 2023; 153:106527. [PMID: 36610216 DOI: 10.1016/j.compbiomed.2022.106527] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/08/2022] [Accepted: 12/31/2022] [Indexed: 01/03/2023]
Abstract
Increasing interest has been attracted in deciphering the potential disease pathogenesis through lncRNA-disease association (LDA) prediction, regarding to the diverse functional roles of lncRNAs in genome regulation. Whilst, computational models and algorithms benefit systematic biology research, even facilitate the classical biological experimental procedures. In this review, we introduce representative diseases associated with lncRNAs, such as cancers, cardiovascular diseases, and neurological diseases. Current publicly available resources related to lncRNAs and diseases have also been included. Furthermore, all of the 64 computational methods for LDA prediction have been divided into 5 groups, including machine learning-based methods, network propagation-based methods, matrix factorization- and completion-based methods, deep learning-based methods, and graph neural network-based methods. The common evaluation methods and metrics in LDA prediction have also been discussed. Finally, the challenges and future trends in LDA prediction have been discussed. Recent advances in LDA prediction approaches have been summarized in the GitHub repository at https://github.com/sheng-n/lncRNA-disease-methods.
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Affiliation(s)
- Nan Sheng
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Lan Huang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
| | - Yuting Lu
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Hao Wang
- Department of Hepatopancreatobiliary Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lili Yang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China; Department of Obstetrics, The First Hospital of Jilin University, Changchun, China
| | - Ling Gao
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Xuping Xie
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Yuan Fu
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, Ceredigion, United Kingdom
| | - Yan Wang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China; School of Artificial Intelligence, Jilin University, Changchun, China.
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Xuan P, Meng X, Gao L, Zhang T, Nakaguchi T. Heterogeneous multi-scale neighbor topologies enhanced drug-disease association prediction. Brief Bioinform 2022; 23:6565159. [PMID: 35393616 DOI: 10.1093/bib/bbac123] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 02/20/2022] [Accepted: 03/15/2022] [Indexed: 12/20/2022] Open
Abstract
MOTIVATION Identifying new uses of approved drugs is an effective way to reduce the time and cost of drug development. Recent computational approaches for predicting drug-disease associations have integrated multi-sourced data on drugs and diseases. However, neighboring topologies of various scales in multiple heterogeneous drug-disease networks have yet to be exploited and fully integrated. RESULTS We propose a novel method for drug-disease association prediction, called MGPred, used to encode and learn multi-scale neighboring topologies of drug and disease nodes and pairwise attributes from heterogeneous networks. First, we constructed three heterogeneous networks based on multiple kinds of drug similarities. Each network comprises drug and disease nodes and edges created based on node-wise similarities and associations that reflect specific topological structures. We also propose an embedding mechanism to formulate topologies that cover different ranges of neighbors. To encode the embeddings and derive multi-scale neighboring topology representations of drug and disease nodes, we propose a module based on graph convolutional autoencoders with shared parameters for each heterogeneous network. We also propose scale-level attention to obtain an adaptive fusion of informative topological representations at different scales. Finally, a learning module based on a convolutional neural network with various receptive fields is proposed to learn multi-view attribute representations of a pair of drug and disease nodes. Comprehensive experiment results demonstrate that MGPred outperforms other state-of-the-art methods in comparison to drug-related disease prediction, and the recall rates for the top-ranked candidates and case studies on five drugs further demonstrate the ability of MGPred to retrieve potential drug-disease associations.
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Affiliation(s)
- Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.,School of Computer Science, Shaanxi Normal University, Xi'an 710062, China
| | - Xiangfeng Meng
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Ling Gao
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Tiangang Zhang
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Toshiya Nakaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba 2638522, Japan
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