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Liu G, Liu J, Liu A. Mitigating sub-synchronous oscillation using intelligent damping control of DFIG based on improved TD3 algorithm with knowledge fusion. Sci Rep 2024; 14:14692. [PMID: 38926443 PMCID: PMC11208430 DOI: 10.1038/s41598-024-65372-y] [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: 01/29/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024] Open
Abstract
The occurrence of sub-synchronous oscillation (SSO) phenomenon in doubly-fed induction generators (DFIGs)-based wind turbines threatens the secure and stable operation of the power grid. Conventional sub-synchronous damping controllers encounter challenges in adapting to the dynamic operating conditions of power systems. This paper introduces an Intelligent Sub-Synchronous Damping Controller (I-SSDC) for DFIGs that integrates deep reinforcement learning (DRL) and knowledge to address the limitations of conventional methods for SSO mitigation. The initial step involves formulating a framework for I-SSDC using the improved twin delayed deep deterministic policy gradient (TD3) algorithm incorporating Softmax. Following this, a surrogate model is constructed, employing Weighted Linear Regression and regularization. This model is designed to identify the predominant influencing factors of SSO, focusing on the selection of the output signal (installation position) to optimize decision-making in I-SSDC. The objective is to enhance the controller's environmental adaptability and interpretability. Moreover, knowledge and experience related to SSOs are integrated into agent training to improve the exploration efficiency of the agent. Case studies under various operating conditions of the test power system validate the efficacy of the proposed I-SSDC in suppressing SSOs.
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Affiliation(s)
- Ge Liu
- College of Automation, Xi'an University of Technology, Xi'an, 710048, China
- Xi'an Aero-Engine Controls Technology Co., Ltd, Xi'an, 710072, China
| | - Jun Liu
- College of Automation, Xi'an University of Technology, Xi'an, 710048, China.
| | - Andong Liu
- College of Automation, Xi'an University of Technology, Xi'an, 710048, China
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Jia C, Wang F, Xing B, Li S, Zhao Y, Li Y, Wang Q. DGAMDA: Predicting miRNA-disease association based on dynamic graph attention network. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2024; 40:e3809. [PMID: 38472636 DOI: 10.1002/cnm.3809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 01/22/2024] [Accepted: 01/27/2024] [Indexed: 03/14/2024]
Abstract
MiRNA (microRNA)-disease association prediction has essential applications for early disease screening. The process of traditional biological experimental validation is both time-consuming and expensive. However, as artificial intelligence technology continues to advance, computational methods have become efficient tools for predicting miRNA-disease associations. These methods often rely on the combination of multiple sources of association data and require improved feature mining. This study proposes a dynamic graph attention-based association prediction model, DGAMDA, which combines feature mapping and dynamic graph attention mechanisms through feature mining on a single miRNA-disease association network. DGAMDA effectively solves the problems of feature heterogeneity and inadequate feature mining by previous static graph attention mechanisms and achieves high-precision feature mining and association scoring prediction. We conducted a five-fold cross-validation experiment and obtained the mean values of Accuracy, Precision, Recall, and F1-score, which were .8986, .8869, .9115, and .8984, respectively. Our proposed model outperforms other advanced models in terms of experimental results, demonstrating its effectiveness in feature mining and association prediction based on a single association network. In addition, our model can also be used to predict miRNAs associated with unknown diseases.
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Affiliation(s)
- ChangXin Jia
- Department of Anesthesiology, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
| | - FuYu Wang
- College of Computer Science and Technology, China University of Petroleum, Qingdao, People's Republic of China
| | - Baoxiang Xing
- Department of Obstetrics, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
| | - ShaoNa Li
- Department of Anesthesiology, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
| | - Yang Zhao
- Department of Anesthesiology, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
| | - Yu Li
- Department of Anesthesiology, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
| | - Qing Wang
- Department of Endocrine and Metabolic, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
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Lu H, Zhang J, Cao Y, Wu S, Wei Y, Yin R. Advances in applications of artificial intelligence algorithms for cancer-related miRNA research. Zhejiang Da Xue Xue Bao Yi Xue Ban 2024; 53:231-243. [PMID: 38650448 PMCID: PMC11057993 DOI: 10.3724/zdxbyxb-2023-0511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/30/2024] [Indexed: 04/25/2024]
Abstract
MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research. Compared to traditional bioinformatic tools, miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy, and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding. Additionally, the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers. In this article, we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms, focusing on the potential of machine learning and deep learning in cancer-related miRNA research.
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Affiliation(s)
- Hongyu Lu
- School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China.
| | - Jia Zhang
- School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China
| | - Yixin Cao
- Department of Medical Oncology, Affiliated Hospital of Jiangsu University, Zhenjiang 212013, Jiangsu Province, China
| | - Shuming Wu
- School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China
| | - Yuan Wei
- School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China.
| | - Runting Yin
- School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China.
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Bauer AN, Majumdar N, Williams F, Rajput S, Pokhrel LR, Cook PP, Akula SM. MicroRNAs: Small but Key Players in Viral Infections and Immune Responses to Viral Pathogens. BIOLOGY 2023; 12:1334. [PMID: 37887044 PMCID: PMC10604607 DOI: 10.3390/biology12101334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/21/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Since the discovery of microRNAs (miRNAs) in C. elegans in 1993, the field of miRNA research has grown steeply. These single-stranded non-coding RNA molecules canonically work at the post-transcriptional phase to regulate protein expression. miRNAs are known to regulate viral infection and the ensuing host immune response. Evolving research suggests miRNAs are assets in the discovery and investigation of therapeutics and diagnostics. In this review, we succinctly summarize the latest findings in (i) mechanisms underpinning miRNA regulation of viral infection, (ii) miRNA regulation of host immune response to viral pathogens, (iii) miRNA-based diagnostics and therapeutics targeting viral pathogens and challenges, and (iv) miRNA patents and the market landscape. Our findings show the differential expression of miRNA may serve as a prognostic biomarker for viral infections in regard to predicting the severity or adverse health effects associated with viral diseases. While there is huge market potential for miRNA technology, the novel approach of using miRNA mimics to enhance antiviral activity or antagonists to inhibit pro-viral miRNAs has been an ongoing research endeavor. Significant hurdles remain in terms of miRNA delivery, stability, efficacy, safety/tolerability, and specificity. Addressing these challenges may pave a path for harnessing the full potential of miRNAs in modern medicine.
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Affiliation(s)
- Anais N. Bauer
- Department of Microbiology & Immunology, Brody School of Medicine, East Carolina University, Greenville, NC 27834, USA; (A.N.B.); (N.M.); (F.W.)
| | - Niska Majumdar
- Department of Microbiology & Immunology, Brody School of Medicine, East Carolina University, Greenville, NC 27834, USA; (A.N.B.); (N.M.); (F.W.)
| | - Frank Williams
- Department of Microbiology & Immunology, Brody School of Medicine, East Carolina University, Greenville, NC 27834, USA; (A.N.B.); (N.M.); (F.W.)
| | - Smit Rajput
- Department of Internal Medicine, Brody School of Medicine, East Carolina University, Greenville, NC 27834, USA;
| | - Lok R. Pokhrel
- Department of Public Health, Brody School of Medicine, East Carolina University, Greenville, NC 27834, USA;
| | - Paul P. Cook
- Department of Internal Medicine, Brody School of Medicine, East Carolina University, Greenville, NC 27834, USA;
| | - Shaw M. Akula
- Department of Microbiology & Immunology, Brody School of Medicine, East Carolina University, Greenville, NC 27834, USA; (A.N.B.); (N.M.); (F.W.)
- Department of Internal Medicine, Brody School of Medicine, East Carolina University, Greenville, NC 27834, USA;
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Yang M, Huang ZA, Gu W, Han K, Pan W, Yang X, Zhu Z. Prediction of biomarker-disease associations based on graph attention network and text representation. Brief Bioinform 2022; 23:6651308. [PMID: 35901464 DOI: 10.1093/bib/bbac298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/28/2022] [Accepted: 06/30/2022] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION The associations between biomarkers and human diseases play a key role in understanding complex pathology and developing targeted therapies. Wet lab experiments for biomarker discovery are costly, laborious and time-consuming. Computational prediction methods can be used to greatly expedite the identification of candidate biomarkers. RESULTS Here, we present a novel computational model named GTGenie for predicting the biomarker-disease associations based on graph and text features. In GTGenie, a graph attention network is utilized to characterize diverse similarities of biomarkers and diseases from heterogeneous information resources. Meanwhile, a pretrained BERT-based model is applied to learn the text-based representation of biomarker-disease relation from biomedical literature. The captured graph and text features are then integrated in a bimodal fusion network to model the hybrid entity representation. Finally, inductive matrix completion is adopted to infer the missing entries for reconstructing relation matrix, with which the unknown biomarker-disease associations are predicted. Experimental results on HMDD, HMDAD and LncRNADisease data sets showed that GTGenie can obtain competitive prediction performance with other state-of-the-art methods. AVAILABILITY The source code of GTGenie and the test data are available at: https://github.com/Wolverinerine/GTGenie.
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Affiliation(s)
- Minghao Yang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, China
| | - Zhi-An Huang
- Center for Computer Science and Information Technology, City University of Hong Kong Dongguan Research Institute, Dongguan, China
| | - Wenhao Gu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, China.,GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Kun Han
- GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Wenying Pan
- GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Xiao Yang
- GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Zexuan Zhu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, China
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Paul S, Ruiz-Manriquez LM, Ambriz-Gonzalez H, Medina-Gomez D, Valenzuela-Coronado E, Moreno-Gomez P, Pathak S, Chakraborty S, Srivastava A. Impact of smoking-induced dysregulated human miRNAs in chronic disease development and their potential use in prognostic and therapeutic purposes. J Biochem Mol Toxicol 2022; 36:e23134. [PMID: 35695328 DOI: 10.1002/jbt.23134] [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: 07/17/2021] [Revised: 04/20/2022] [Accepted: 05/29/2022] [Indexed: 12/14/2022]
Abstract
MicroRNAs (miRNAs) are evolutionary conserved small noncoding RNA molecules with a significant ability to regulate gene expression at the posttranscriptional level either through translation repression or messenger RNA degradation. miRNAs are differentially expressed in various pathophysiological conditions, affecting the course of the disease by modulating several critical target genes. As the persistence of irreversible molecular changes caused by cigarette smoking is central to the pathogenesis of various chronic diseases, several studies have shown its direct correlation with the dysregulation of different miRNAs, affecting numerous essential biological processes. This review provides an insight into the current status of smoking-induced miRNAs dysregulation in chronic diseases such as COPD, atherosclerosis, pulmonary hypertension, and different cancers and explores the diagnostic/prognostic potential of miRNA-based biomarkers and their efficacy as therapeutic targets.
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Affiliation(s)
- Sujay Paul
- Tecnologico de Monterrey, School of Engineering and Sciences, Campus Queretaro, Av. Epigmenio Gonzalez, San Pablo, Queretaro, Mexico
| | - Luis M Ruiz-Manriquez
- Tecnologico de Monterrey, School of Engineering and Sciences, Campus Queretaro, Av. Epigmenio Gonzalez, San Pablo, Queretaro, Mexico
| | - Hector Ambriz-Gonzalez
- Tecnologico de Monterrey, School of Engineering and Sciences, Campus Queretaro, Av. Epigmenio Gonzalez, San Pablo, Queretaro, Mexico
| | - Daniel Medina-Gomez
- Tecnologico de Monterrey, School of Engineering and Sciences, Campus Queretaro, Av. Epigmenio Gonzalez, San Pablo, Queretaro, Mexico
| | - Estefania Valenzuela-Coronado
- Tecnologico de Monterrey, School of Engineering and Sciences, Campus Queretaro, Av. Epigmenio Gonzalez, San Pablo, Queretaro, Mexico
| | - Paloma Moreno-Gomez
- Tecnologico de Monterrey, School of Engineering and Sciences, Campus Queretaro, Av. Epigmenio Gonzalez, San Pablo, Queretaro, Mexico
| | - Surajit Pathak
- Department of Medical Biotechnology, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI), Chettinad Academy of Research and Education (CARE), Kelambakkam, Chennai, Tamil Nadu, India
| | - Samik Chakraborty
- Division of Nephrology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Aashish Srivastava
- Section of Bioinformatics, Clinical Laboratory, Haukeland University Hospital, Bergen, Norway.,Department of Clinical Science, University of Bergen, Bergen, Norway
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