Готові списки джерел за темами / Drug disease

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Добірка наукової літератури з теми "Drug disease"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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Статті в журналах з теми "Drug disease"

Jyotsna, M., and Y. Hemalatha. "Drug–Drug, Drug–Disease and Disease–Disease Interactions in COVID-19 with Cardiovascular Diseases (CVDs)." Indian Journal of Cardiovascular Disease in Women WINCARS 5, no. 03 (2020): 216–22. http://dx.doi.org/10.1055/s-0040-1716786.

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AbstractCoronaviruses are a large family of single positive-stranded, enveloped RNA viruses that can infect many animal species and humans. Human coronaviruses can be divided based on their pathogenicity. Globally so far, over nine million people have tested COVID-19 positive, of which, 4, 25,000 are in India. The FDA for the prevention or treatment of COVID-19 has approved no drugs or biologics. Numerous other antiviral agents, immunotherapies, and vaccines continue to be investigated and developed as potential therapies. Searching for effective therapies for COVID-19 infection is a complex p

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Yalkızımı, Selcan, and Ümit Şentürk. "Study on Drug Repurposing for ALS Treatment Using Pre-trained Knowledge Graph Embeddings: Methods and Findings." Düzce Üniversitesi Bilim ve Teknoloji Dergisi 13, no. 1 (2025): 317–32. https://doi.org/10.29130/dubited.1507832.

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In this study, research has been conducted using pre-trained knowledge graph embedding for drug repurposing in treating ALS (Amyotrophic Lateral Sclerosis), and its results have been presented. Drug repurposing studies for ALS have been carried out through two main methods: disease-drug relationship and genes-drugs relationship. Drug repurposing recommendations for ALS have been provided by predicting connections between disease and drug entities on the DRKG (Drug Repurposing Knowledge Graph). The findings obtained from the study have been evaluated by comparing them with the list of clinical

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Zhou, Xu, Enyu Dai, Qian Song, et al. "In silico drug repositioning based on drug-miRNA associations." Briefings in Bioinformatics 21, no. 2 (2019): 498–510. http://dx.doi.org/10.1093/bib/bbz012.

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Abstract Drug repositioning has become a prevailing tactic as this strategy is efficient, economical and low risk for drug discovery. Meanwhile, recent studies have confirmed that small-molecule drugs can modulate the expression of disease-related miRNAs, which indicates that miRNAs are promising therapeutic targets for complex diseases. In this study, we put forward and verified the hypothesis that drugs with similar miRNA profiles may share similar therapeutic properties. Furthermore, a comprehensive drug–drug interaction network was constructed based on curated drug-miRNA associations. Thro

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Xuan, Ping, Yangkun Cao, Tiangang Zhang, Xiao Wang, Shuxiang Pan, and Tonghui Shen. "Drug repositioning through integration of prior knowledge and projections of drugs and diseases." Bioinformatics 35, no. 20 (2019): 4108–19. http://dx.doi.org/10.1093/bioinformatics/btz182.

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Abstract Motivation Identifying and developing novel therapeutic effects for existing drugs contributes to reduction of drug development costs. Most of the previous methods focus on integration of the heterogeneous data of drugs and diseases from multiple sources for predicting the candidate drug–disease associations. However, they fail to take the prior knowledge of drugs and diseases and their sparse characteristic into account. It is essential to develop a method that exploits the more useful information to predict the reliable candidate associations. Results We present a method based on no

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Kim, Yoonbee, and Young-Rae Cho. "Predicting Drug–Gene–Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning." Biomedicines 11, no. 7 (2023): 1998. http://dx.doi.org/10.3390/biomedicines11071998.

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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 tripl

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Jefferson, James W., John H. Greist, Judith Carroll, and Margaret Baudhuin. "Drug-drug and drug-disease interactions with nonsteroidal anti-inflammatory drugs." American Journal of Medicine 81, no. 5 (1986): 948. http://dx.doi.org/10.1016/0002-9343(86)90382-7.

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Craig Brater, D. "Drug-drug and drug-disease interactions with nonsteroidal anti-inflammatory drugs." American Journal of Medicine 80, no. 1 (1986): 62–77. http://dx.doi.org/10.1016/0002-9343(86)90933-2.

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Xuan, Ping, Yingying Song, Tiangang Zhang, and Lan Jia. "Prediction of Potential Drug–Disease Associations through Deep Integration of Diversity and Projections of Various Drug Features." International Journal of Molecular Sciences 20, no. 17 (2019): 4102. http://dx.doi.org/10.3390/ijms20174102.

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Identifying new indications for existing drugs may reduce costs and expedites drug development. Drug-related disease predictions typically combined heterogeneous drug-related and disease-related data to derive the associations between drugs and diseases, while recently developed approaches integrate multiple kinds of drug features, but fail to take the diversity implied by these features into account. We developed a method based on non-negative matrix factorization, DivePred, for predicting potential drug–disease associations. DivePred integrated disease similarity, drug–disease associations,

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Kim, Jiwon W., and Paula V. Phongsamran. "Drug-Induced Liver Disease and Drug Use Considerations in Liver Disease." Journal of Pharmacy Practice 22, no. 3 (2009): 278–89. http://dx.doi.org/10.1177/0897190008328696.

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Chronic liver disease encompasses a large number of hepatic disorders. One of the most important etiologies of liver disease is drug-induced liver disease, which is the leading cause of liver failure in patients referred for liver transplantation in the United States. Drug-induced liver disease can present in all forms of acute and chronic liver disease with highly variable clinical presentations. There is no effective treatment for most drug-induced liver disease and the recognition and prevention of drug-induced liver disease remain the most important management strategy. Drug dosing in pati

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Le, Duc-Hau, and Duc-Hau Le. "ID:2047 Drug respositioning by integrating known disease-gene and drug-target associations." Biomedical Research and Therapy 4, S (2017): 76. http://dx.doi.org/10.15419/bmrat.v4is.281.

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Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs, diseases and different approaches. Depending on where the discovery of drug-disease relationships comes from, proposed computational methods can be categorized as either ‘drug-based’ or ‘disease-based’. The proposed methods are usually based on an assumption that similar drugs can be used for similar diseases to identify new indications

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Дисертації з теми "Drug disease"

Durrant, Jacob Devin. "Computational drug design applied to neglected disease." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3404614.

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Thesis (Ph. D.)--University of California, San Diego, 2010.<br>Title from first page of PDF file (viewed June 7, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (leaves 213-239).

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Lickteig, Andrew Joseph. "Drug Metabolizing Enzyme, Drug Transporter Expression And Drug Disposition Are Altered In Models Of Inflammatory Liver Disease." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/193836.

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Correct dosing in pharmacotherapeutics is based on the idea that too much of a drug will cause toxicity, while too little will result in failure to elicit the desired response. A major factor in the ability of a patient to handle any dose of a drug is the capacity to metabolize and eliminate that drug from the body. For the vast majority of drugs, the liver plays a key role in determining the rate at which drugs are eliminated. First, drugs must be taken up across the cell membrane into hepatocytes by uptake transporters. Once inside the hepatocyte, biotransformation enzymes metabolize and con

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Grosset, Katherine Anne. "Therapy concordance and drug adherence in Parkinson's disease." Thesis, University of Glasgow, 2005. http://theses.gla.ac.uk/2341/.

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Chapter 1 gives an overview of the relevance of studying therapy adherence in Parkinson’s disease. Chapter 2 examines drug induced neurological syndromes and considers the validity of patients’ concerns about taking prescribed medications. Chapter 3 compares different methods of assessing therapy adherence. Chapter 4 studies factors associated with sub-optimal medicine usage in 54 patients. Chapter 5 reports a study of patient perceived involvement with management decisions and an assessment of satisfaction with the movement disorder service in 107 patients. Chapter 6 explores patients’ belief

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Sappy, Immaculate. "Ribonucleic Acids in Disease Etiology and Drug Discovery." University of Toledo Health Science Campus / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=mco1566562465233197.

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TONON, FEDERICA. "Exploring drug molecular effects in cancer disease models." Doctoral thesis, Università degli Studi di Trieste, 2016. http://hdl.handle.net/11368/2908483.

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Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. As usually HCC diagnosis occurs in the advanced stage of the disease, available treatments such as resection, liver transplant, or local ablation are poorly if not at all effective. Also systemic chemotherapy has limited effectiveness. The only drug that can prolong patient survival is Sorafenib; unfortunately, however, the extent of increased survival is very modest being of few months. Together the above considerations clearly indicate that the development of novel therapeutic approaches for HCC are

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Shams, Tahiatul. "Solute Carrier Transporters in drug-drug/herb interactions and their molecular regulation in disease." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/20342.

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Solute Carrier Transporters (SLCs) are a group of influx transporters responsible for the cellular uptake of endogenous substances and exogenous molecules including a number of clinically important drugs. They are widely expressed in epithelium throughout the body, where they play essential roles in determining the disposition and elimination of these substances. They have been recognized as the crucial determinants to drug pharmacokinetics in humans. The dysfunction of SLC transporters contributes to the pathophysiology of several diseases, as well as largely impacts on drug pharmacokinetic p

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Freeman, J. G. "Therapeutic modalities in liver disease." Thesis, University of Newcastle Upon Tyne, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378854.

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Ibrahim, N. "Drug-related problems (DRPs) in children with kidney disease." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1448344/.

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Introduction: Medicines are used with the intention of benefitting from their effect. The effects of medicines can also be undesirable and potentially lead to harm. A drug-related problem (DRP) is a term used to describe problem(s) that exist in the use of medicines. There remains a distinct paucity of data on the epidemiology of DRPs in children with kidney disease. Aim: To investigate the epidemiology of DRPs in children with kidney disease in clinical practice at tertiary Paediatric Nephrology units. Methods: Study 1: Prospective observational study on the characteristics of DRPs in hospita

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Terrasso, Ana Paula Barreto. "Neural cell models for disease modeling and drug discovery." Doctoral thesis, Universidade Nova de Lisboa, Instituto de Tecnologia Química e Biológica António Xavier, 2018. http://hdl.handle.net/10362/98065.

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"Neurological disorders are a major public health problem and are expected to rise dramatically together with the higher life expectancy and the shift towards an ageing society. Current therapeutic options can only ameliorate some of the symptoms and there are no effective treatments to target pathological mechanisms and stop disease progression. The human brain complexity hampers the understanding of the brain functioning at the molecular, cellular, and pathophysiological levels for many neurological disorders. This highlights the need for new brain models, which can contribute to unveil mole

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Nyren-Erickson, Erin Kathryn. "In Vitro Detection of Disease Biomarkers and Drug Contaminants." Diss., North Dakota State University, 2013. https://hdl.handle.net/10365/27018.

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In recent years the rising cost and increased regulation within the U.S. healthcare system have caused medical laboratory tests to become more costly and more frequently required. As a result, insurance premiums are rising, and small independent laboratories are threatened with closure as their already narrow margins dwindle. Concurrently, there have been several incidents of contaminants and impurities in pharmaceutical drugs causing hundreds of deaths and thousands of illnesses. These challenges substantiate the need for simple and cost-effective screening tests for the presence of disease b

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Книги з теми "Drug disease"

Lippincott Williams & Wilkins., ed. Disease & drug consult. Wolters Kluwer/Lippincott Williams & Wilkins Health, 2009.

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Lippincott Williams & Wilkins., ed. Disease & drug consult. Wolters Kluwer/Lippincott Williams & Wilkins, 2009.

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Huml, Raymond A., ed. Rare Disease Drug Development. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78605-2.

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D, Cooper J. Allen, ed. Drug-induced pulmonary disease. W. B. Saunders, 1990.

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Farrell, Geoffrey C. Drug-induced liver disease. Churchill Livingstone, 1994.

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Neil, Kaplowitz, and DeLeve Laurie D. 1955-, eds. Drug-induced liver disease. Marcel Dekker, 2003.

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Neil, Kaplowitz, and DeLeve Laurie D. 1955-, eds. Drug-induced liver disease. Marcel Dekker, 2003.

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Neil, Kaplowitz, and DeLeve Laurie D. 1955-, eds. Drug-induced liver disease. 2nd ed. Informa Healthcare USA, 2007.

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Christina, Wolfson, and Canadian Coordinating Office for Health Technology Assessment., eds. Drug treatments for Alzheimer's disease. Canadian Coordinating Office for Health Technology Assessment, 2000.

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Droogmans, Steven. Drug-induced valvular heart disease. Nova Science Publishers, 2010.

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Частини книг з теми "Drug disease"

Gieschke, Ronald, and Daniel Serafin. "Drug-Disease Modeling." In Development of Innovative Drugs via Modeling with MATLAB. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39765-3_5.

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Shung, Dennis L., and Joseph K. Lim. "Drug-Induced Liver Injury." In Liver Disease. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98506-0_1.

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Hamilton, James P., and Jacqueline M. Laurin. "Drug-Induced Cholestasis." In Cholestatic Liver Disease. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-118-5_2.

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Gardenswartz, Mark H., Jan P. Goldberg, and Robert W. Schrier. "Drug-Induced Nephrotoxicity." In Chronic Renal Disease. Springer US, 1985. http://dx.doi.org/10.1007/978-1-4684-4826-9_37.

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Szyper-Kravitz, Martine, Yehuda Shoenfeld, and Abihai Lucas Hernández. "Drug-Induced Autoimmunity." In Autoimmune Disease Diagnosis. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-69895-8_11.

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Wang, Xiaohui, Thomas A. Cochran, Mark R. Hutchinson, Hang Yin, and Linda R. Watkins. "Drug Addiction." In Microglia in Health and Disease. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1429-6_12.

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Flecha, Antonette, Johnathan Voss, and Diana Hao. "Drug-Nutrient Interactions." In Nutrition in Kidney Disease. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44858-5_9.

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O'Donnell, James T., James J. O'Donnell, Donald H. Marks, and Paul N. Danese. "Drug-Induced Liver Disease." In O'Donnell's Drug Injury, 5th ed. CRC Press, 2025. https://doi.org/10.1201/9781003615323-17.

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Sakamaki, Fumio. "DLI Induced by Antiarrhythmic Drug and Antimicrobial Drug: What Are the Characteristics of DLI in Antiarrhythmic Drugs and Antimicrobial Drugs?" In Respiratory Disease Series: Diagnostic Tools and Disease Managements. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4466-3_14.

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Burkhardt, Heinrich. "Parkinson’s Disease." In Drug Therapy for the Elderly. Springer Vienna, 2012. http://dx.doi.org/10.1007/978-3-7091-0912-0_13.

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Тези доповідей конференцій з теми "Drug disease"

Vottas, Marios, Fotis Aisopos, Konstantinos Bougiatiotis, and Anastasia Krithara. "Predicting Multi-Type Drug-Drug Interactions Using a Disease-Specific Knowledge Graph." In 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2025. https://doi.org/10.1109/cbms65348.2025.00157.

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Ruggeri, Lorenzo, Eva Viesi, Andrea Betti, and Rosalba Giugno. "Unveiling Novel Gene-Drug-Disease Interactions with GDD-BERT." In 2025 IEEE 13th International Conference on Healthcare Informatics (ICHI). IEEE, 2025. https://doi.org/10.1109/ichi64645.2025.00014.

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Fuadi, Mukhlish, Adhi Dharma Wibawa, Edwin Nugroho Njoto, and Ghulam Asrofi Buntoro. "Identification of Potential Drug-Drug Interactions Using EMR Text-Mining on Atherosclerotic Heart Disease Patients." In 2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT). IEEE, 2024. http://dx.doi.org/10.1109/iaict62357.2024.10617451.

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Zhang, Yidan, Lei Duan, Huiru Zheng, et al. "DDTExplainer: Mining Drug-Disease Therapeutic Mechanisms based on GNN Explainability." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822060.

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Mishra, Prerna, and Shubham Sharma. "MedNote BERT: Modeling Medical Notes and Predicting Drug-Disease Interactions." In 2024 International Conference on Decision Aid Sciences and Applications (DASA). IEEE, 2024. https://doi.org/10.1109/dasa63652.2024.10836617.

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Schmieg, Jaime, Alicia Williams, and Pavlos Vlachos. "Magnetic Drug Targeting: Drug Delivery in Large Vasculature." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-193157.

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Magnetic drug targeting (MDT) is a novel drug delivery method with potential to dramatically revolutionize clinical approaches of the treatment of many diseases. In fact, MDT has been proposed for ailments ranging from vascular disease to cancer [1, 2]. Conventional drug delivery methods utilize large doses of medication to account for the dispersion of the drug throughout the body in the hope that a sufficient concentration of medicine arrives at the diseased site. Unfortunately, many medications can have caustic effects on healthy systems leaving patients with discomfort, weakened immunity o

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Chen, Huiyuan, and Jing Li. "Learning Data-Driven Drug-Target-Disease Interaction via Neural Tensor Network." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/477.

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Precise medicine recommendations provide more effective treatments and cause fewer drug side effects. A key step is to understand the mechanistic relationships among drugs, targets, and diseases. Tensor-based models have the ability to explore relationships of drug-target-disease based on large amount of labeled data. However, existing tensor models fail to capture complex nonlinear dependencies among tensor data. In addition, rich medical knowledge are far less studied, which may lead to unsatisfied results. Here we propose a Neural Tensor Network (NeurTN) to assist personalized medicine trea

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Wang, Binyu, Yunhao Zhang, Hongyan Ye, et al. "Drug-target and Drug-disease Association Prediction based on Drug-target-disease Network and Multi-task Learning." In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2023. http://dx.doi.org/10.1109/bibm58861.2023.10386051.

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Twycross, Martin, and Toby Carter. "Innovative design and features in Rare Disease Registries." In International Drug Repurposing Conference 2025. ScienceOpen, 2025. https://doi.org/10.14293/idr.25.002mt.

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M, Saranya, Arockia Xavier Annie R, and Geetha T V. "Relation Extraction between Biomedical Entities from Literature using Semi- Supervised Learning Approach." In 10th International Conference on Natural Language Processing (NLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112306.

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Now-a-days, people around the world are infected by many new diseases. The cost of developing or discovering a new drug for the newly discovered disease is very high and prolonged process. These could be eliminated with the help of already existing resources. To identify the candidates from the existing drugs, we need to extract the relation between the drug, target and disease by textming a large-scale literature. Recently, computational approaches which is used for identifying the relationships between the entities in biomedical domain are appearing as an active area of research for drug dis

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Звіти організацій з теми "Drug disease"

Dr. Edward L. D'Antonio, Dr Edward L. D'Antonio. Early-Stage Drug Discovery for Chagas' Disease. Experiment, 2018. http://dx.doi.org/10.18258/11365.

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Paul, Satashree. Turning Back the Sickle Cell Disease: A New Drug into Play. Science Repository OÜ, 2021. http://dx.doi.org/10.31487/sr.blog.38.

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Researchers at the Fulcrum Therapeutics developed a bioavailable drug candidate called FTX 6058 – (a novel small molecular fetal haemoglobin inducer for sickle cell disease) that can restore the body’s ability to produce fetal haemoglobin

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Dr. Edward L. D'Antonio, Dr Edward L. D'Antonio. Molecular target-based focused screen for Chagas' disease therapeutic drug discovery. Experiment, 2023. http://dx.doi.org/10.18258/50252.

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Grueso-Navarro, Elena, Leticia Rodríguez-Alcolado, Ángel Arias, Emilio J. Laserna-Mendieta та Alfredo J. Lucendo. Influence of HLA-DQA1*05 allele in the response to anti-TNFα drugs in inflammatory bowel diseases. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.2.0076.

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Review question / Objective: Do patients with inflammatory bowel disease and treated with any anti-TNFα drug who had the HLA-DQA1*05 allele (in heterozygosis or homozygosis) have lower response or persistence to those drugs than patients without HLA-DQA1*05 allele? Condition being studied: Inflammatory bowel diseases (IBD), which includes Crohn’s disease (CD) and ulcerative colitis (UC), is a chronic inflammatory condition that may affect any part of the digestive tract (CD) or be limited to the colon (UC). While the specific aetiology of IBD remains unknown, it is believed to involve a comple

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Bunn, Sarah. COVID-19 therapies. Parliamentary Office of Science and Technology, 2020. http://dx.doi.org/10.58248/rr34.

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This article was updated on 1 May and again on 6 July. Since its original publication on 17 April, the number of COVID-19 clinical trials has increased from 524 to 2,378. There is no cure for COVID-19. Researchers are testing existing drugs to see if they act against SARS-CoV-2 or alleviate the symptoms of the disease. New drugs are also in development, but this is at a very early stage. Results from trials on existing drugs have already been reported with some positive findings. Dexamethasone is a cheap steroid drug that reduces the risk of death of ventilated patients by 35% and by 20% for p

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Wendel, Hans-Guido. In vivo RNAi Library Screen to Identify Mediators of Disease Progression and Drug Resistance in CML. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada469370.

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Chambers, Colton. Variation of drug timing and its effect on "susceptibility gap" larvae in canine heartworm disease treatment. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-1584.

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Som Chaudhury, Sutapa, Jaya Sil, Sabyashachi Mishra, and Chitrangada Das Mukhopadhyay. A side-chain tripeptide based PEGylated block copolymer: a potential drug candidate in Alzheimer’s disease translational therapeutics. Peeref, 2023. http://dx.doi.org/10.54985/peeref.2303p6424311.

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Lampley, Katrice, and Nicole Therrien. Rural Arizona Medication Therapy Management (RAzMTM) Program Field Notes. National Center for Chronic Disease Prevention and Health Promotion (U.S.)., 2023. http://dx.doi.org/10.15620/cdc:126172.

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These Field Notes summarize the Rural Arizona Medication Therapy Management (RAzMTM) Program’s work toward assessing the effectiveness of telehealth pharmacy services in improving health indicators for people who are medically underserved, managing chronic disease, and reducing adverse drug events in patients.

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Zhang, Yu, Chaoliang Sun, Hengxi Xu, et al. Connectivity-Based Subtyping of De Novo Parkinson Disease: Biomarkers, Medication Effects and Longitudinal Progression. Progress in Neurobiology, 2024. http://dx.doi.org/10.60124/j.pneuro.2024.10.04.

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Parkinson's disease (PD) is characterized by divergent clinical symptoms and prognosis, suggesting the presence of distinct subtypes. Identifying these subtypes is crucial for understanding the underlying pathophysiology, predicting disease progression, and developing personalized treatments. In this study, we propose a connectivity-based subtyping approach, which measures each patient's deviation from the reference structural covariance networks established in healthy controls. Using data from the Parkinson's Progression Markers Initiative, we identified two distinct subtypes of de novo PD pa

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