Bibliografías temáticas / Disease progression modeling

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Literatura académica sobre el tema "Disease progression modeling"

Autor: Grafiati
Publicado: 25 de mayo de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Disease progression modeling"

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Reeve, Russell, Lei Pang, Bradley Ferguson, Michael O’Kelly, Seth Berry, and Wei Xiao. "Rheumatoid Arthritis Disease Progression Modeling." Therapeutic Innovation & Regulatory Science 47, no. 6 (2013): 641–50. http://dx.doi.org/10.1177/2168479013499571.

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Inoue, Lurdes Y. T., Ruth Etzioni, Christopher Morrell, and Peter Müller. "Modeling Disease Progression With Longitudinal Markers." Journal of the American Statistical Association 103, no. 481 (2008): 259–70. http://dx.doi.org/10.1198/016214507000000356.

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Plevritis, Sylvia K. "Modeling disease progression in outcomes research." Academic Radiology 6 (January 1999): S132—S133. http://dx.doi.org/10.1016/s1076-6332(99)80108-1.

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Young, Alexandra L., Felix J. S. Bragman, Bojidar Rangelov, et al. "Disease Progression Modeling in Chronic Obstructive Pulmonary Disease." American Journal of Respiratory and Critical Care Medicine 201, no. 3 (2020): 294–302. http://dx.doi.org/10.1164/rccm.201908-1600oc.

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Rooney, William D., Yosef A. Berlow, William T. Triplett, et al. "Modeling disease trajectory in Duchenne muscular dystrophy." Neurology 94, no. 15 (2020): e1622-e1633. http://dx.doi.org/10.1212/wnl.0000000000009244.

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ObjectiveTo quantify disease progression in individuals with Duchenne muscular dystrophy (DMD) using magnetic resonance biomarkers of leg muscles.MethodsMRI and magnetic resonance spectroscopy (MRS) biomarkers were acquired from 104 participants with DMD and 51 healthy controls using a prospective observational study design with patients with DMD followed up yearly for up to 6 years. Fat fractions (FFs) in vastus lateralis and soleus muscles were determined with 1H MRS. MRI quantitative T2 (qT2) values were measured for 3 muscles of the upper leg and 5 muscles of the lower leg. Longitudinal ch
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Zhou, Jiayu, Jun Liu, Vaibhav A. Narayan, and Jieping Ye. "Modeling disease progression via multi-task learning." NeuroImage 78 (September 2013): 233–48. http://dx.doi.org/10.1016/j.neuroimage.2013.03.073.

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Mehdipour Ghazi, Mostafa, Mads Nielsen, Akshay Pai, et al. "Robust parametric modeling of Alzheimer’s disease progression." NeuroImage 225 (January 2021): 117460. http://dx.doi.org/10.1016/j.neuroimage.2020.117460.

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Sun, Zhaonan, Soumya Ghosh, Ying Li, et al. "A probabilistic disease progression modeling approach and its application to integrated Huntington’s disease observational data." JAMIA Open 2, no. 1 (2019): 123–30. http://dx.doi.org/10.1093/jamiaopen/ooy060.

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Abstract Objective Chronic diseases often have long durations with slow, nonlinear progression and complex, and multifaceted manifestation. Modeling the progression of chronic diseases based on observational studies is challenging. We developed a framework to address these challenges by building probabilistic disease progression models to enable better understanding of chronic diseases and provide insights that could lead to better disease management. Materials and Methods We developed a framework to build probabilistic disease progression models using observational medical data. The framework
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Gomeni, Roberto, Monica Simeoni, Marina Zvartau-Hind, Michael C. Irizarry, Daren Austin, and Michael Gold. "Modeling Alzheimer's disease progression using the disease system analysis approach." Alzheimer's & Dementia 8, no. 1 (2011): 39–50. http://dx.doi.org/10.1016/j.jalz.2010.12.012.

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Cook, Sarah F., and Robert R. Bies. "Disease Progression Modeling: Key Concepts and Recent Developments." Current Pharmacology Reports 2, no. 5 (2016): 221–30. http://dx.doi.org/10.1007/s40495-016-0066-x.

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Tesis sobre el tema "Disease progression modeling"

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Huszti, Ella. "Markov modeling of disease progression and mortality." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=95060.

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Prognostic studies of progression and mortality in different diseases are essential to understand the role of particular prognostic factors and, thus, improve prognosis and ultimately help selecting appropriate interventions. Yet, such studies often face serious limitations of available data and/or of the existing statistical methods. One difficulty concerns separating the effects of putative prognostic factors on different clinical endpoints or “competing events” such as e.g. disease recurrence vs. recurrence-free death, or death due to disease vs. death due to other causes. This issue becom
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Sauty, Benoît. "Multimodal modelling of Alzheimer's Disease progression." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS348.

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La maladie d'Alzheimer (MA) est une pathologie multi-facette qui peut être surveillée grâce à une grande variété de modalités de données. Cette thèse vise à exploiter des données longitudinales multimodales, principalement des données d'imagerie et des tests cognitifs, pour fournir une description statistique de la progression de la MA et permettre une prévision individuelle de la dégradation future. Les modèles de progression à effet-mixtes de la maladie (DPMs) sont couramment utilisés pour ces tâches. Dans ce contexte, notre première contribution remet en question l'hypothèse fréquente selon
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McHugh, Kevin J. "Age-related macular degeneration: interventional tissue engineering and predictive modeling of disease progression." Thesis, Boston University, 2014. https://hdl.handle.net/2144/19690.

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Thesis (Ph.D.)--Boston University<br>Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in people over the age of 50. As many as 50 million people are affected by AMD worldwide and prevalence is expected to continue to rise due to an aging population. There are two forms of the disease, dry (geographic atrophy) and wet (choroidal neovascularization), both of which result in retinal degeneration and central vision loss. Although anti-vascular endothelial growth factor therapies are moderately successful at treating the wet form, there are no treatments current
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Shelton, Morgan Griffin. "Modeling the Effects of Supercomplex Formation and Stress Response on Alzheimer’S Disease Progression." W&M ScholarWorks, 2019. https://scholarworks.wm.edu/etd/1563899025.

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Alzheimer’s disease is a specific form of dementia characterized by the aggregation of Amyloid-β plaques and tau tangles. New research has found that the formation of these aggregates occurs after dysregulation of respiratory activity and the production of radical oxygen species. Proteomic data shows that these changes are also related to unique gene expression patterns. We investigate the impact of these findings on new therapeutic options via metabolic flux analysis of sirtuin stress response pathways and respiratory supercomplex formation. Our results indicate CRISPR Cas-based gene therapy
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Conrado, Daniela J., Timothy Nicholas, Kuenhi Tsai, et al. "Dopamine Transporter Neuroimaging as an Enrichment Biomarker in Early Parkinson's Disease Clinical Trials: A Disease Progression Modeling Analysis." WILEY, 2018. http://hdl.handle.net/10150/626602.

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Given the recognition that disease-modifying therapies should focus on earlier Parkinson's disease stages, trial enrollment based purely on clinical criteria poses significant challenges. The goal herein was to determine the utility of dopamine transporter neuroimaging as an enrichment biomarker in early motor Parkinson's disease clinical trials. Patient-level longitudinal data of 672 subjects with early-stage Parkinson's disease in the Parkinson's Progression Markers Initiative (PPMI) observational study and the Parkinson Research Examination of CEP-1347 Trial (PRECEPT) clinical trial were ut
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Hubbard, Rebecca Allana. "Modeling a non-homogeneous Markov process via time transformation /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/9607.

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Bône, Alexandre. "Learning adapted coordinate systems for the statistical analysis of anatomical shapes. Applications to Alzheimer's disease progression modeling." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS273.

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Cette thèse construit des systèmes de coordonnées pour formes, c'est-à-dire des espaces métriques de dimension finie où les formes sont représentées par des vecteurs. Construire de tels systèmes de coordonnées permet de faciliter l'analyse statistique de collections de formes. Notre motivation finale est de prédire et de sous-typer la maladie d'Alzheimer, en se basant notamment sur des marqueurs ainsi extraits de banques d'images médicales du cerveau. Même si de telles banques sont longitudinales, la variabilité qu’elles renferment reste principalement due à la variabilité inter-individuelle i
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Robertson, Chadia L. "Analysis of the Role of Astrocyte Elevated Gene-1 in Normal Liver Physiology and in the Onset and Progression of Hepatocellular Carcinoma." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3573.

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First identified over a decade ago, Astrocyte Elevated Gene-1 (AEG-1) has been studied extensively due to early reports of its overexpression in various cancer cell lines. Research groups all over the globe including our own have since identified AEG-1 overexpression in cancers of diverse lineages including cancers of the liver, colon, skin, prostate, breast, lung, esophagus, neurons and neuronal glia as compared to matched normal tissue. A comprehensive and convincing body of data currently points to AEG-1 as an essential component, critical to the progression and perhaps onset of cancer. AEG
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dePillis-Lindheim, Lydia. "Disease Correlation Model: Application to Cataract Incidence in the Presence of Diabetes." Scholarship @ Claremont, 2013. http://scholarship.claremont.edu/scripps_theses/294.

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Diabetes is a major risk factor for the development of cataract [3,14,20,22]. In this thesis, we create a model that allows us to understand the incidence of one disease in the context of another; in particular, cataract in the presence of diabetes. The World Health Organization's Vision 2020 blindness-prevention initiative administers surgeries to remove cataracts, the leading cause of blindness worldwide [24]. One of the geographic areas most impacted by cataract-related blindness is Sub-Saharan Africa. In order to plan the number of surgeries to administer, the World Health Organization use
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Laranjeira, Simão. "Modelling the progression of neurodegenerative diseases." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:ebb621d0-e4e6-405e-9e54-ba385c3ebd0a.

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Neurodegenerative disease is an umbrella term for pathologies that primarily damage neurons. As their incidence increases with age it is becoming of a greater concern for the west, due to its aging population. Due to their chronic nature and the difficulty to create reliable and reproducible animal models of these diseases their pathophysiologies are still poorly understood. For all these reasons, a mathematical modelling approach is suggested. The methodology of the work here consisted of identifying the state of the art models that describe the healthy behaviour of cells (e.g. metabolism and
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Libros sobre el tema "Disease progression modeling"

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Aspden, Richard, and Jenny Gregory. Morphology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199668847.003.0011.

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The study of joint morphology can help us to understand the risk factors for osteoarthritis (OA), how it progresses, and aids in developing imaging biomarkers for study of the disease. OA results in gross structural changes in affected joints. Growth of osteophytes, deformation of joint components, and loss of joint space where cartilage has broken down are all characteristics of the disorder. Certain bone shapes as well as malalignment predispose people to future OA, or may be a marker for early OA. Geometrical measures, such as the alpha angle or Wiberg’s CE angle, used to be the primary too
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Capítulos de libros sobre el tema "Disease progression modeling"

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Camargo, Anyela, and Jan T. Kim. "Disease Progression Modeling." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_221.

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Ibarra, Manuel, Marianela Lorier, and Iñaki F. Trocóniz. "Pharmacometrics: Disease Progression Modeling." In The ADME Encyclopedia. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84860-6_174.

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Ibarra, Manuel, Marianela Lorier, and Iñaki F. Trocóniz. "Pharmacometrics: Disease Progression Modeling." In The ADME Encyclopedia. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-51519-5_174-1.

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Ng, Kenney, Mohamed Ghalwash, Prithwish Chakraborty, et al. "Data-Driven Disease Progression Modeling." In Health Informatics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07912-2_17.

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Oxtoby, Neil P. "Data-Driven Disease Progression Modeling." In Machine Learning for Brain Disorders. Springer US, 2012. http://dx.doi.org/10.1007/978-1-0716-3195-9_17.

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AbstractIntense debate in the neurology community before 2010 culminated in hypothetical models of Alzheimer’s disease progression: a pathophysiological cascade of biomarkers, each dynamic for only a segment of the full disease timeline. Inspired by this, data-driven disease progression modeling emerged from the computer science community with the aim to reconstruct neurodegenerative disease timelines using data from large cohorts of patients, healthy controls, and prodromal/at-risk individuals. This chapter describes selected highlights from the field, with a focus on utility for understandin
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Mould, Diane R. "Modeling the Progression of Disease." In Pharmacokinetics in Drug Development. Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-7937-7_3.

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Self, Steve, and Yudi Pawitan. "Modeling a Marker of Disease Progression and Onset of Disease." In AIDS Epidemiology. Birkhäuser Boston, 1992. http://dx.doi.org/10.1007/978-1-4757-1229-2_11.

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Venkatraghavan, Vikram, Esther E. Bron, Wiro J. Niessen, and Stefan Klein. "A Discriminative Event Based Model for Alzheimer’s Disease Progression Modeling." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59050-9_10.

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Gervelmeyer, Julius, Sarah Müller, Kerol Djoumessi, et al. "Interpretable-by-Design Deep Survival Analysis for Disease Progression Modeling." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72117-5_47.

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Tomasevic, Smiljana, and Nenad Filipović. "Use Case: Agent-Based Modeling for Atherosclerotic Plaque Progression in Carotid Arteries." In In Silico Clinical Trials for Cardiovascular Disease. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60044-9_9.

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Actas de conferencias sobre el tema "Disease progression modeling"

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Yu, Minghang, Linlin Gao, Dan Wu, and Jianhua Wang. "RoRankNet: Modeling Ordinal Progression in Alzheimer’s Disease with Plackett-Luce Ranking and Hard Instance Learning." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822539.

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Yue, Fangye, Rui Qiu, Jinhu Li, and Guohe Li. "Privacy-Preserving Federated Learning Framework for Disease Progression Prediction via Temporal-Aware Large Language Modeling." In 2025 International Conference on Sensor-Cloud and Edge Computing System (SCECS). IEEE, 2025. https://doi.org/10.1109/scecs65243.2025.11065627.

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Guazzo, Alessandro, Enrico Longato, Gian Paolo Fadini, Giovanni Sparacino, Rema Padman, and Barbara Di Camillo. "Characterization of Chronic Kidney Disease Progression in Patients with Diabetes via Group-Based Multi-Trajectory Modeling." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10781792.

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Prasanna Kumar, L. Lakshmi, Vankadara Sampath Kumar, G. Ashok Kumar, Y. Nagendar, Nithya M, and A. Athiraja. "AI-Driven Predictive Modeling for Early Detection and Progression Monitoring of Chronic Kidney Disease Using Multimodal Data." In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG). IEEE, 2024. https://doi.org/10.1109/icstsdg61998.2024.11026441.

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Kaur, Arpanpreet, and Gotte Ranjith Kumar. "Bridging the Gap in MS Diagnosis: Predictive Modelling of Disease Progression Using Logistic Regression and KNN." In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. IEEE, 2025. https://doi.org/10.1109/otcon65728.2025.11070479.

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Yang, Xi, Ge Gao, and Min Chi. "Hierarchical Apprenticeship Learning for Disease Progression Modeling." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/265.

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Disease progression modeling (DPM) plays an essential role in characterizing patients' historical pathways and predicting their future risks. Apprenticeship learning (AL) aims to induce decision-making policies by observing and imitating expert behaviors. In this paper, we investigate the incorporation of AL-derived patterns into DPM, utilizing a Time-aware Hierarchical EM Energy-based Subsequence (THEMES) AL approach. To the best of our knowledge, this is the first study incorporating AL-derived progressive and interventional patterns for DPM. We evaluate the efficacy of this approach in a ch
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Wang, Xulong, Jun Qi, Yun Yang, and Po Yang. "A Survey of Disease Progression Modeling Techniques for Alzheimer's Diseases." In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN). IEEE, 2019. http://dx.doi.org/10.1109/indin41052.2019.8972091.

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Pearson, Ronald K., Robert J. Kingan, and Alan Hochberg. "Disease progression modeling from historical clinical databases." In Proceeding of the eleventh ACM SIGKDD international conference. ACM Press, 2005. http://dx.doi.org/10.1145/1081870.1081974.

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Sukkar, R., E. Katz, Yanwei Zhang, D. Raunig, and B. T. Wyman. "Disease progression modeling using Hidden Markov Models." In 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6346556.

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Liu, Xiaoli, Jiali Li, and Peng Cao. "Modeling Disease Progression with Deep Neural Networks." In ISICDM 2020: The Fourth International Symposium on Image Computing and Digital Medicine. ACM, 2020. http://dx.doi.org/10.1145/3451421.3451429.

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Informes sobre el tema "Disease progression modeling"

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Barhak, Jacob. Supplemental Information: The Reference Model is a Multi-Scale Ensemble Model of COVID-19. Outbreak, 2021. http://dx.doi.org/10.34235/b7eaa32b-1a6b-444f-9848-76f83f5a733c.

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The COVID-19 pandemic has accelerated research worldwide and resulted in a large number of computational models and initiatives. Models were mostly aimed at forecast and resulted in different predictions partially since models were based on different assumptions. In fact the idea that a computational model is just an assumption attempting to explain a phenomenon has not been sufficiently explored. Moreover, the ability to combine models has not been fully realized. The Reference Model for disease progression was performing this task for years for diabetes models and recently started modeling C
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Ruvinsky, Alicia, Maria Seale, R. Salter, and Natàlia Garcia-Reyero. An ontology for an epigenetics approach to prognostics and health management. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/46632.

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Techniques in prognostics and health management have advanced considerably in the last few decades, enabled by breakthroughs in computational methods and supporting technologies. These predictive models, whether data-driven or physics-based, target the modeling of a system’s aggregate performance. As such, they generalize assumptions about the modelled system’s components, and are thus limited in their ability to represent individual components and the dynamic environmental factors that affect composite system health. To address this deficiency, we have developed an epigenetics-inspired knowle
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