1
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Zhang Y, Li X. Empowering Graph Neural Networks with Block-Based Dual Adaptive Deep Adjustment for Drug Resistance-Related NcRNA Discovery. J Chem Inf Model 2024; 64:3537-3547. [PMID: 38523272 DOI: 10.1021/acs.jcim.3c01973] [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: 03/26/2024]
Abstract
Drug resistance to chemotherapeutic agents remains a formidable challenge in cancer treatment, significantly impacting treatment efficacy. Extensive research has exposed the intimate involvement of noncoding RNAs (ncRNAs) in conferring resistance to cancer drugs. Understanding the intricate associations between ncRNAs and drug resistance is of pivotal importance in advancing clinical interventions and expediting drug development. However, traditional biological experimental methods are hampered by limitations, such as labor intensiveness, time consumption, and constraints in scalability. Addressing these challenges necessitates the development of efficient computational methods for the accurate prediction of potential ncRNA-drug resistance associations (NDRA). However, most existing predictive models primarily focus on known ncRNA-drug resistance associations, often neglecting the critical aspect of similarity information between ncRNAs and drug resistance. This oversight may hinder the accuracy of characterizing these associations. To overcome the limitations of existing computational models, we proposed B-NDRA, a computational framework designed for the discovery of drug resistance-related ncRNA. Initially, we constructed a heterogeneous graph that integrates ncRNA-drug resistance pairs, leveraging both known associations and similarity fusion information between ncRNAs and drug resistance. Subsequently, we employed an attention mechanism to aggregate local features of graph nodes following a dimensionality reduction of node features. Further, a graph neural network (GNN) facilitated the learning of global node embeddings. Notably, the integration of dual adaptive deep adjustment architectures, encompassing intrablock and interblock methodologies, enabled efficient extraction of global features while balancing local and global features. Finally, B-NDRA employed a multilayer perceptron to predict associations between ncRNAs and drug resistance. Through rigorous 5-fold cross-validation, B-NDRA achieved average AUC, AUPR, Accuracy, Precision, Recall, and F1-score values of 92.2%, 91.9%, 84.88%, 86.9%, 82.37%, and 84.44%, respectively. Furthermore, comparative evaluations were conducted on established models, namely, GAEMDA, GRPAMDA, and LRGCPND. The results, obtained through three distinct 5-fold cross-validation strategies, demonstrated a notable performance improvement across almost all metrics for our B-NDRA. Specific case studies targeting Doxorubicin and Imatinib further validated the practicality of our B-NDRA in discovering potential NDRA. These results confirm the potential of our B-NDRA as a valuable tool in advancing cancer research and therapeutic development. The source code and data set of B-NDRA can be found at https://github.com/XuanLi1145/B-NDRA.
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Affiliation(s)
- Yi Zhang
- Guilin University of Technology, Guilin 541004, China
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin 541004, China
| | - Xuanzhao Li
- Guilin University of Technology, Guilin 541004, China
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin 541004, China
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2
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Fan Y, Zhang C, Hu X, Huang Z, Xue J, Deng L. SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning. Brief Bioinform 2024; 25:bbae231. [PMID: 38754409 PMCID: PMC11097980 DOI: 10.1093/bib/bbae231] [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: 01/31/2024] [Revised: 04/15/2024] [Accepted: 04/30/2024] [Indexed: 05/18/2024] Open
Abstract
Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug-gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph convolutional network (GCN)-based models' state-of-the-art performance in prediction, their reliance on supervised learning makes them vulnerable to data sparsity, a common challenge in drug discovery, further complicating model development. In this study, we propose SGCLDGA, a novel computational model leveraging graph neural networks and contrastive learning to predict unknown drug-gene associations. SGCLDGA employs GCNs to extract vector representations of drugs and genes from the original bipartite graph. Subsequently, singular value decomposition (SVD) is employed to enhance the graph and generate multiple views. The model performs contrastive learning across these views, optimizing vector representations through a contrastive loss function to better distinguish positive and negative samples. The final step involves utilizing inner product calculations to determine association scores between drugs and genes. Experimental results on the DGIdb4.0 dataset demonstrate SGCLDGA's superior performance compared with six state-of-the-art methods. Ablation studies and case analyses validate the significance of contrastive learning and SVD, highlighting SGCLDGA's potential in discovering new drug-gene associations. The code and dataset for SGCLDGA are freely available at https://github.com/one-melon/SGCLDGA.
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Affiliation(s)
- Yanhao Fan
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Che Zhang
- School of software, Xinjiang University, 830046, Urumqi, China
| | - Xiaowen Hu
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Zhijian Huang
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Jiameng Xue
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
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3
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Pudova E, Kobelyatskaya A, Emelyanova M, Snezhkina A, Fedorova M, Pavlov V, Guvatova Z, Dalina A, Kudryavtseva A. Non-Coding RNAs and the Development of Chemoresistance to Docetaxel in Prostate Cancer: Regulatory Interactions and Approaches Based on Machine Learning Methods. Life (Basel) 2023; 13:2304. [PMID: 38137905 PMCID: PMC10744715 DOI: 10.3390/life13122304] [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: 09/18/2023] [Revised: 11/30/2023] [Accepted: 12/05/2023] [Indexed: 12/24/2023] Open
Abstract
Chemotherapy based on taxane-class drugs is the gold standard for treating advanced stages of various oncological diseases. However, despite the favorable response trends, most patients eventually develop resistance to this therapy. Drug resistance is the result of a combination of different events in the tumor cells under the influence of the drug, a comprehensive understanding of which has yet to be determined. In this review, we examine the role of the major classes of non-coding RNAs in the development of chemoresistance in the case of prostate cancer, one of the most common and socially significant types of cancer in men worldwide. We will focus on recent findings from experimental studies regarding the prognostic potential of the identified non-coding RNAs. Additionally, we will explore novel approaches based on machine learning to study these regulatory molecules, including their role in the development of drug resistance.
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Affiliation(s)
- Elena Pudova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | | | - Marina Emelyanova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Anastasiya Snezhkina
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Maria Fedorova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Vladislav Pavlov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Zulfiya Guvatova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
- Russian Clinical Research Center for Gerontology, Pirogov Russian National Research Medical University, Ministry of Healthcare of the Russian Federation, 129226 Moscow, Russia
| | - Alexandra Dalina
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Anna Kudryavtseva
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
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4
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Hu X, Liu D, Zhang J, Fan Y, Ouyang T, Luo Y, Zhang Y, Deng L. A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations. Brief Bioinform 2023; 24:bbad410. [PMID: 37985451 DOI: 10.1093/bib/bbad410] [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: 08/01/2023] [Revised: 10/07/2023] [Accepted: 10/25/2023] [Indexed: 11/22/2023] Open
Abstract
Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.
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Affiliation(s)
- Xiaowen Hu
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| | - Dayun Liu
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| | - Jiaxuan Zhang
- Department of Electrical and Computer Engineering, University of California, San Diego,92093 CA, USA
| | - Yanhao Fan
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| | - Tianxiang Ouyang
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| | - Yue Luo
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| | - Yuanpeng Zhang
- school of software, Xinjiang University, 830046 Urumqi, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
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5
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Rakicevic L. DNA and RNA Molecules as a Foundation of Therapy Strategies for Treatment of Cardiovascular Diseases. Pharmaceutics 2023; 15:2141. [PMID: 37631355 PMCID: PMC10459020 DOI: 10.3390/pharmaceutics15082141] [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: 06/30/2023] [Revised: 07/27/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
There has always been a tendency of medicine to take an individualised approach to treating patients, but the most significant advances were achieved through the methods of molecular biology, where the nucleic acids are in the limelight. Decades of research of molecular biology resulted in setting medicine on a completely new platform. The most significant current research is related to the possibilities that DNA and RNA analyses can offer in terms of more precise diagnostics and more subtle stratification of patients in order to identify patients for specific therapy treatments. Additionally, principles of structure and functioning of nucleic acids have become a motive for creating entirely new therapy strategies and an innovative generation of drugs. All this also applies to cardiovascular diseases (CVDs) which are the leading cause of mortality in developed countries. This review considers the most up-to-date achievements related to the use of translatory potential of DNA and RNA in treatment of cardiovascular diseases, and considers the challenges and prospects in this field. The foundations which allow the use of translatory potential are also presented. The first part of this review focuses on the potential of the DNA variants which impact conventional therapies and on the DNA variants which are starting points for designing new pharmacotherapeutics. The second part of this review considers the translatory potential of non-coding RNA molecules which can be used to formulate new generations of therapeutics for CVDs.
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Affiliation(s)
- Ljiljana Rakicevic
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Vojvode Stepe 444a, 11042 Belgrade, Serbia
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6
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Zhang P, Wang Z, Sun W, Xu J, Zhang W, Wu K, Wong L, Li L. RDRGSE: A Framework for Noncoding RNA-Drug Resistance Discovery by Incorporating Graph Skeleton Extraction and Attentional Feature Fusion. ACS OMEGA 2023; 8:27386-27397. [PMID: 37546619 PMCID: PMC10398708 DOI: 10.1021/acsomega.3c02763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 07/06/2023] [Indexed: 08/08/2023]
Abstract
Identifying noncoding RNAs (ncRNAs)-drug resistance association computationally would have a marked effect on understanding ncRNA molecular function and drug target mechanisms and alleviating the screening cost of corresponding biological wet experiments. Although graph neural network-based methods have been developed and facilitated the detection of ncRNAs related to drug resistance, it remains a challenge to explore a highly trusty ncRNA-drug resistance association prediction framework, due to inevitable noise edges originating from the batch effect and experimental errors. Herein, we proposed a framework, referred to as RDRGSE (RDR association prediction by using graph skeleton extraction and attentional feature fusion), for detecting ncRNA-drug resistance association. Specifically, starting with the construction of the original ncRNA-drug resistance association as a bipartite graph, RDRGSE took advantage of a bi-view skeleton extraction strategy to obtain two types of skeleton views, followed by a graph neural network-based estimator for iteratively optimizing skeleton views aimed at learning high-quality ncRNA-drug resistance edge embedding and optimal graph skeleton structure, jointly. Then, RDRGSE adopted adaptive attentional feature fusion to obtain final edge embedding and identified potential RDRAs under an end-to-end pattern. Comprehensive experiments were conducted, and experimental results indicated the significant advantage of a skeleton structure for ncRNA-drug resistance association discovery. Compared with state-of-the-art approaches, RDRGSE improved the prediction performance by 6.7% in terms of AUC and 6.1% in terms of AUPR. Also, ablation-like analysis and independent case studies corroborated RDRGSE generalization ability and robustness. Overall, RDRGSE provides a powerful computational method for ncRNA-drug resistance association prediction, which can also serve as a screening tool for drug resistance biomarkers.
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Affiliation(s)
- Ping Zhang
- Hubei
Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Zilin Wang
- Hubei
Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Weicheng Sun
- Hubei
Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jinsheng Xu
- Hubei
Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Weihan Zhang
- Hubei
Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Kun Wu
- Department
of Biochemistry, University of California
Riverside, Riverside, California 92521, United States
| | - Leon Wong
- Guangxi
Key Lab of Human-Machine Interaction and Intelligent Decision, Guangxi Academy of Sciences, Nanning 530007, China
- Institute
of Machine Learning and Systems Biology, School of Electronics and
Information Engineering, Tongji University, Shanghai 200092, China
| | - Li Li
- Hubei
Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
- Hubei
Hongshan Laboratory, Huazhong Agricultural
University, Wuhan 430070, China
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7
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Gao M, Shang X. Identification of associations between lncRNA and drug resistance based on deep learning and attention mechanism. Front Microbiol 2023; 14:1147778. [PMID: 37180267 PMCID: PMC10169643 DOI: 10.3389/fmicb.2023.1147778] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 04/04/2023] [Indexed: 05/16/2023] Open
Abstract
Introduction Abnormal lncRNA expression can lead to the resistance of tumor cells to anticancer drugs, which is a crucial factor leading to high cancer mortality. Studying the relationship between lncRNA and drug resistance becomes necessary. Recently, deep learning has achieved promising results in predicting biomolecular associations. However, to our knowledge, deep learning-based lncRNA-drug resistance associations prediction has yet to be studied. Methods Here, we proposed a new computational model, DeepLDA, which used deep neural networks and graph attention mechanisms to learn lncRNA and drug embeddings for predicting potential relationships between lncRNAs and drug resistance. DeepLDA first constructed similarity networks for lncRNAs and drugs using known association information. Subsequently, deep graph neural networks were utilized to automatically extract features from multiple attributes of lncRNAs and drugs. These features were fed into graph attention networks to learn lncRNA and drug embeddings. Finally, the embeddings were used to predict potential associations between lncRNAs and drug resistance. Results Experimental results on the given datasets show that DeepLDA outperforms other machine learning-related prediction methods, and the deep neural network and attention mechanism can improve model performance. Dicsussion In summary, this study proposes a powerful deep-learning model that can effectively predict lncRNA-drug resistance associations and facilitate the development of lncRNA-targeted drugs. DeepLDA is available at https://github.com/meihonggao/DeepLDA.
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Affiliation(s)
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
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8
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Deng L, Fan Z, Xiao X, Liu H, Zhang J. Dual-Channel Heterogeneous Graph Neural Network for Predicting microRNA-Mediated Drug Sensitivity. J Chem Inf Model 2022; 62:5929-5937. [PMID: 36413746 DOI: 10.1021/acs.jcim.2c01060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Many studies have confirmed that microRNAs (miRNAs) are mediated in the sensitivity of tumor cells to anticancer drugs. MiRNAs are emerging as a type of promising therapeutic targets to overcome drug resistance. However, there is limited attention paid to the computational prediction of the associations between miRNAs and drug sensitivity. In this work, we proposed a heterogeneous network-based representation learning method to predict miRNA-drug sensitivity associations (DGNNMDA). An miRNA-drug heterogeneous network was constructed by integrating miRNA similarity network, drug similarity network, and experimentally validated miRNA-drug sensitivity associations. Next, we developed a dual-channel heterogeneous graph neural network model to perform feature propagation among the homogeneous and heterogeneous nodes so that our method can learn expressive representations for miRNA and drug nodes. On two benchmark datasets, our method outperformed other seven competitive methods. We also verified the effectiveness of the feature propagations on homogeneous and heterogeneous nodes. Moreover, we have conducted two case studies to verify the reliability of our methods and tried to reveal the regulatory mechanism of miRNAs mediated in drug sensitivity. The source code and datasets are freely available at https://github.com/19990915fzy/DGNNMDA.
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Affiliation(s)
- Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha410083, China
| | - Ziyu Fan
- School of Computer Science and Engineering, Central South University, Changsha410083, China
| | - Xiaojun Xiao
- Software School, Xinjiang University, Urumqi830091, China
| | - Hui Liu
- School of Computer Science and Technology, Nanjing Tech University, Nanjing211816, China
| | - Jiaxuan Zhang
- Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, California92161, United States
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9
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Zheng J, Qian Y, He J, Kang Z, Deng L. Graph Neural Network with Self-Supervised Learning for Noncoding RNA-Drug Resistance Association Prediction. J Chem Inf Model 2022; 62:3676-3684. [PMID: 35838124 DOI: 10.1021/acs.jcim.2c00367] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Noncoding RNA(ncRNA) is closely related to drug resistance. Identifying the association between ncRNA and drug resistance is of great significance for drug development. Methods based on biological experiments are often time-consuming and small-scale. Therefore, developing computational methods to distinguish the association between ncRNA and drug resistance is urgent. We develop a computational framework called GSLRDA to predict the association between ncRNA and drug resistance in this work. First, the known ncRNA-drug resistance associations are modeled as a bipartite graph of ncRNA and drug. Then, GSLRDA uses the light graph convolutional network (lightGCN) to learn the vector representation of ncRNA and drug from the ncRNA-drug bipartite graph. In addition, GSLRDA uses different data augmentation methods to generate different views for ncRNA and drug nodes and performs self-supervised learning, further improving the quality of learned ncRNA and drug vector representations through contrastive learning between nodes. Finally, GSLRDA uses the inner product to predict the association between ncRNA and drug resistance. To the best of our knowledge, GSLRDA is the first to apply self-supervised learning in association prediction tasks in the field of bioinformatics. The experimental results show that GSLRDA takes an AUC value of 0.9101, higher than the other eight state-of-the-art models. In addition, case studies including two drugs further illustrate the effectiveness of GSLRDA in predicting the association between ncRNA and drug resistance. The code and data sets of GSLRDA are available at https://github.com/JJZ-code/GSLRDA.
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Affiliation(s)
- Jingjing Zheng
- School of Software, Xinjiang University, Urumqi 830091, China
| | - Yurong Qian
- School of Software, Xinjiang University, Urumqi 830091, China
| | - Jie He
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Zerui Kang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Lei Deng
- School of Software, Xinjiang University, Urumqi 830091, China.,School of Computer Science and Engineering, Central South University, Changsha 410083, China
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10
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Liu Y, Cheng X, Li H, Hui S, Zhang Z, Xiao Y, Peng W. Non-Coding RNAs as Novel Regulators of Neuroinflammation in Alzheimer's Disease. Front Immunol 2022; 13:908076. [PMID: 35720333 PMCID: PMC9201920 DOI: 10.3389/fimmu.2022.908076] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 05/09/2022] [Indexed: 01/04/2023] Open
Abstract
Alzheimer’s disease (AD) is one of the most common causes of dementia. Although significant breakthroughs have been made in understanding the progression and pathogenesis of AD, it remains a worldwide problem and a significant public health burden. Thus, more efficient diagnostic and therapeutic strategies are urgently required. The latest research studies have revealed that neuroinflammation is crucial in the pathogenesis of AD. Non-coding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs), microRNAs (miRNAs), circular RNAs (circRNAs), PIWI-interacting RNAs (piRNAs), and transfer RNA-derived small RNAs (tsRNAs), have been strongly associated with AD-induced neuroinflammation. Furthermore, several ongoing pre-clinical studies are currently investigating ncRNA as disease biomarkers and therapeutic interventions to provide new perspectives for AD diagnosis and treatment. In this review, the role of different types of ncRNAs in neuroinflammation during AD are summarized in order to improve our understanding of AD etiology and aid in the translation of basic research into clinical practice.
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Affiliation(s)
- Yuqing Liu
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Mental Disorder, Changsha, China
| | - Xin Cheng
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Mental Disorder, Changsha, China
| | - Hongli Li
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Mental Disorder, Changsha, China
| | - Shan Hui
- Department of Geratology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Zheyu Zhang
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Mental Disorder, Changsha, China
| | - Yang Xiao
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, Changsha, China.,Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Weijun Peng
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Mental Disorder, Changsha, China
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