Готові списки джерел за темами / Application Shiny

Зміст

  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Добірка наукової літератури з теми "Application Shiny"

Автор: Grafiati
Опубліковано: 26 жовтня 2024

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Статті в журналах з теми "Application Shiny"

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Shirokovskikh, Margarita S. "THE APPLICATION OF SHINY MATERIALS IN TAPESTRY: FROM DECOR TO CONCEPT." Architecton: Proceedings of Higher Education, no. 2(70) (June 29, 2020): 21. http://dx.doi.org/10.47055/1990-4126-2020-2(70)-21.

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Анотація:
This article is the first ever attempt to study and summarize the extensive material on the use of shiny threads in tapestry art. The circle of artists using shine as an expressive medium in their artworks is identified. Methods of creating shiny surfaces in this art form and their symbolic meanings are outlined.
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2

Morota, Gota. "79 Statistical Graphics and Interactive Visualization in Animal Science." Journal of Animal Science 99, Supplement_3 (October 8, 2021): 45. http://dx.doi.org/10.1093/jas/skab235.079.

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Анотація:
Abstract Statistical graphics has advanced significantly in recent years with the development of statistical computing tools that allow us to create reports dynamically, facilitate reproducible research, and explore data interactively. In particular, data visualization is a fundamental aspect of big data analysis in animal science. However, the static nature of standard visualization limits the information that can be displayed and extracted. The objectives of this hands-on workshop are to learn how to utilize interactive visualization and investigate both global and local structures of graphs with useful zooming in and zooming out capabilities. We will use the Shiny R package, which is a web application framework for R. A Shiny application has great potential to deliver interactive data analysis and visualization in a web browser. Yet there is limited application of this type of tool in agricultural science. We will learn the capabilities of R Shiny and its use with example applications in animal science and how to aid scientific discoveries and decision-making processes using interactive data exploration tools. After taking this workshop, the participants will be able to understand the concept of R Shiny and develop a web-based interactive visualization tool. The interactive and integrative data visualization features embedded in Shiny applications offer a new resource for users to readily extract extensive information from complex data.
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3

Morota, Gota. "312 Statistical graphics and interactive visualization in animal science." Journal of Animal Science 98, Supplement_4 (November 3, 2020): 45–46. http://dx.doi.org/10.1093/jas/skaa278.083.

Повний текст джерела
Анотація:
Abstract Statistical graphics has advanced significantly in recent years with the development of statistical computing tools that allow us to create reports dynamically, facilitate reproducible research, and explore data interactively. In particular, data visualization is a fundamental aspect of big data analysis in animal science. However, the static nature of standard visualization limits the information that can be displayed and extracted. The objectives of this hands-on workshop are to learn how to utilize interactive visualization and investigate both global and local structures of graphs with useful zooming in and zooming out capabilities. We will use the Shiny R package, which is a web application framework for R. A Shiny application has great potential to deliver interactive data analysis and visualization in a web browser. Yet there is limited application of this type of tool in agricultural science. We will learn the capabilities of R Shiny and its use with example applications in animal science and how to aid scientific discoveries and decision-making processes using interactive data exploration tools. After taking this workshop, the participants will be able to understand the concept of R Shiny and develop a web-based interactive visualization tool. The interactive and integrative data visualization features embedded in Shiny applications offer a new resource for users to readily extract extensive information from complex data.
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4

Jagla, Bernd, Valentina Libri, Claudia Chica, Vincent Rouilly, Sebastien Mella, Michel Puceat, and Milena Hasan. "SCHNAPPs - Single Cell sHiNy APPlication(s)." Journal of Immunological Methods 499 (December 2021): 113176. http://dx.doi.org/10.1016/j.jim.2021.113176.

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5

Kemperman, Lauren, and Matthew N. McCall. "miRcomp-Shiny: Interactive assessment of qPCR-based microRNA quantification and quality control algorithms." F1000Research 6 (November 23, 2017): 2046. http://dx.doi.org/10.12688/f1000research.13098.1.

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Анотація:
The miRcomp-Shiny web application allows interactive performance assessments and comparisons of qPCR-based microRNA expression and quality estimation methods using a benchmark data set. This work is motivated by two distinct use cases: (1) selection of methodology and quality thresholds for use analyzing one's own data, and (2) comparison of novel expression estimation algorithms with currently-available methodology. The miRcomp-Shiny application is implemented in the R/Shiny language and can be installed on any operating system on which R can be installed. It is made freely available as part of the miRcomp package (version 1.3.3 and later) available through the Bioconductor project at: http://bioconductor.org/packages/miRcomp. The web application is hosted at https://laurenkemperman.shinyapps.io/mircomp/. A detailed description of how to use the web application is available at: http://lkemperm.github.io/miRcomp_shiny_app
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Li, Jianfeng, Bowen Cui, Yuting Dai, Ling Bai, and Jinyan Huang. "BioInstaller: a comprehensive R package to construct interactive and reproducible biological data analysis applications based on the R platform." PeerJ 6 (October 31, 2018): e5853. http://dx.doi.org/10.7717/peerj.5853.

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Анотація:
The increase in bioinformatics resources such as tools/scripts and databases poses a great challenge for users seeking to construct interactive and reproducible biological data analysis applications. Here, we propose an open-source, comprehensive, flexible R package named BioInstaller that consists of the R functions, Shiny application, the HTTP representational state transfer application programming interfaces, and a docker image. BioInstaller can be used to collect, manage and share various types of bioinformatics resources and perform interactive and reproducible data analyses based on the extendible Shiny application with Tom’s Obvious, Minimal Language and SQLite format databases. The source code of BioInstaller is freely available at our lab website, http://bioinfo.rjh.com.cn/labs/jhuang/tools/bioinstaller, the popular package host GitHub, https://github.com/JhuangLab/BioInstaller, and the Comprehensive R Archive Network, https://CRAN.R-project.org/package=BioInstaller. In addition, a docker image can be downloaded from DockerHub (https://hub.docker.com/r/bioinstaller/bioinstaller).
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7

Oberti, Mauricio, and Iosif Vaisman. "shiny-pred: a server for the prediction of protein disordered regions." F1000Research 8 (February 28, 2019): 230. http://dx.doi.org/10.12688/f1000research.17669.1.

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Анотація:
Intrinsically disordered proteins or intrinsically disordered regions (IDR) are segments within a protein chain lacking a stable three-dimensional structure under normal physiological conditions. Accurate prediction of IDRs is challenging due to their genome wide occurrence and low ratio of disordered residues, making them a difficult target for traditional classification techniques. Existing computational methods mostly rely on sequence profiles to improve accuracy, which is time consuming and computationally expensive. The shiny-pred application is an ab initio sequence-only disorder predictor implemented in R/Shiny language. In order to make predictions, it uses convolutional neural network models, trained using PDB sequence data. It can be installed on any operating system on which R can be installed and run locally. A public version of the web application can be accessed at https://gmu-binf.shinyapps.io/shiny-pred
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8

Bernardo, Nicholas D., and Gretchen A. Macht. "Learning ‘Learning Curves’ with R Shiny." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (September 2021): 1567–71. http://dx.doi.org/10.1177/1071181321651119.

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Анотація:
Learning curves are fundamental in understanding individual task performance, with ubiquitous implementation in task assignments, worker scheduling, team formulation, etc., in domains bridging from manufacturing to healthcare. With a broad range of applicability, it is critical that students conceptualize, visualize, and build learning curves to activate that knowledge for effective decision-making. This paper describes a hands-on experiential approach for teaching learning curves that utilizes building LEGO® sets with mathematical formulation and data visualization in an open-source R Shiny application. The R Shiny application was designed to educate and inform students of their curve status while automating the power curve fitting calculations. The proposed methodology appeals and applies to students of all ages and was preliminarily field-tested in two collegiate courses and a K-4 after-school program. This paper introduces this approach and the R Shiny app, while future work includes quantifying improved learning.
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9

Marusich, Laura R., and Jonathan Z. Bakdash. "rmcorrShiny: A web and standalone application for repeated measures correlation." F1000Research 10 (November 8, 2021): 697. http://dx.doi.org/10.12688/f1000research.55027.2.

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Анотація:
We describe a web and standalone Shiny app for calculating the common, linear within-individual association for repeated assessments of paired measures with multiple individuals: repeated measures correlation (rmcorr). This tool makes rmcorr more widely accessible, providing a graphical interface for performing and visualizing the output of analysis with rmcorr. In contrast to rmcorr, most widely used correlation techniques assume paired data are independent. Incorrectly analyzing repeated measures data as independent will likely produce misleading results. Using aggregation or separate models to address the issue of independence may obscure meaningful patterns and will also tend to reduce statistical power. rmcorrShiny (repeated measures correlation Shiny) provides a simple and accessible solution for computing the repeated measures correlation. It is available at: https://lmarusich.shinyapps.io/shiny_rmcorr/.
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10

Marusich, Laura R., and Jonathan Z. Bakdash. "rmcorrShiny: A web and standalone application for repeated measures correlation." F1000Research 10 (July 30, 2021): 697. http://dx.doi.org/10.12688/f1000research.55027.1.

Повний текст джерела
Анотація:
We describe a web and standalone Shiny app for calculating the common, linear within-individual association for repeated assessments of paired measures with multiple individuals: repeated measures correlation (rmcorr). This tool makes rmcorr more widely accessible, providing a graphical interface for performing and visualizing the output of analysis with rmcorr. In contrast to rmcorr, most widely used correlation techniques assume paired data are independent. Incorrectly analyzing repeated measures data as independent will likely produce misleading results. Using aggregation or separate models to address the issue of independence may obscure meaningful patterns and will also tend to reduce statistical power. rmcorrShiny (repeated measures correlation Shiny) provides a simple and accessible solution for computing the repeated measures correlation. It is available at: https://lmarusich.shinyapps.io/shiny_rmcorr/.
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Більше джерел

Дисертації з теми "Application Shiny"

1

Biesiada, Jacek. "Shiny Application for Enrichment and Topological Pathway Analysis." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595846446399689.

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2

Nicvert, Lisa. "Méthodes statistiques et outils logiciels pour l'analyse et l'inférence de réseaux écologiques et le traitement de données multi-espèces." Electronic Thesis or Diss., Lyon 1, 2024. http://www.theses.fr/2024LYO10130.

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Анотація:
Les interactions entre espèces dans les communautés écologiques sont complexes : de nombreuses espèces peuvent interagir les unes avec les autres de façons variées et à différentes échelles spatiales et temporelles. De plus, ces réseaux d'interactions sont la résultante de multiples causes, engendrent de multiples processus et ont des conséquences parfois indirectes transmises au travers de la structure du réseau. Cette complexité nécessite une diversité d'approches pour comprendre les déterminants des interactions et prédire leurs effets dans les systèmes écologiques. Cette thèse étudie plusieurs aspects des réseaux d'interactions écologiques par une approche méthodologique qui se concentre sur la description, l'évaluation et le développement de méthodes statistiques et d'outils logiciels. Dans une première partie, j'étudie les causes de la structure des réseaux d'interactions en me concentrant sur les niches d'interactions et en utilisant la notion de concordance des traits entre espèces. Pour cela, j'utilise des méthodes de la famille de l'analyse des correspondances et j'applique et j'étends des méthodes de mise à l'échelle réciproque à l'analyse de réseaux bipartites. J'applique ces méthodes à l'analyse d'un réseau d'interactions plantes-frugivores d'une forêt de montagne péruvienne et je montre que les traits des espèces peuvent être reliés à leur largeur de niche. Dans une deuxième partie, j'étudie les conséquences des interactions au travers de leur influence sur la répartition spatio-temporelle des espèces. Pour cela, j'utilise des processus de Hawkes multivariés pour analyser des données de pièges photographiques. J'illustre ces modèles sur cinq mammifères de la savane sud-africaine et je montre des attractions et évitements entre plusieurs de ces espèces à courte échelle spatio-temporelle. Dans une troisième partie, je me penche sur l'analyse de données collectées par pièges photographiques. Je développe un package R pour nettoyer et standardiser ces données à l'usage du programme Snapshot Safari, ainsi qu'une application Shiny destinée à un usage plus général pour visualiser de données de façon interactive et reproductible. Cette thèse présente des méthodes statistiques et outils logiciels pour analyser des données écologiques complexes et améliorer la compréhension des réseaux d'interactions. Ces résultats ouvrent des perspectives nouvelles concernant l'analyse de données écologiques ainsi que les développement méthodologique en écologie
Interactions between species in ecological communities are complex: many species can interact with each other in a variety of ways and at different spatial and temporal scales. Moreover, these interaction networks are the result of multiple causes, generate multiple processes and can have indirect effects transmitted through the structure of the network. This complexity calls for a variety of approaches to understand the determinants of interactions and predict their effects in ecological systems. This thesis studies several aspects of ecological interaction networks using a methodological approach that focuses on the description, evaluation and development of statistical methods and software tools. In a first part, I study causes of the structure of interaction networks, focusing on interaction niches and using the notion of trait matching between species. To this end, I use methods from the correspondence analysis family and apply and extend reciprocal scaling methods to the analysis of bipartite networks. I apply these methods to the analysis of a plant-frugivore interaction network in a Peruvian montane forest, and show that species traits can be related to their niche width. In a second part, I study the consequences of interactions through their influence on the spatio-temporal distribution of species. To this end, I use multivariate Hawkes processes to analyze camera trap data. I illustrate these models on five mammals from the South African savanna, showing attraction and avoidance between several of these species at a short spatio-temporal scale. In a third part, I consider camera trap data analysis. I develop a R package to clean and standardize camera trap data intended for the Snapshot Safari program, as well as a Shiny application intended for a more general use to visualize data in an interactive and reproducible way. This thesis presents statistical methods and software tools to analyze complex ecological data and improve our understanding of interaction networks. These results open new perspectives on ecological data analysis and methodological development in ecology
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3

Ankrah, Stephanie. "Protective materials for sporting applications : football shin guards." Thesis, University of Birmingham, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288869.

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4

Sertori, Matteo. "Studio e realizzazione dell'interfaccia grafica per un applicativo Health." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21539/.

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5

Mukherjee, Sapna Shukla [Verfasser], Rainer [Gutachter] Heintzmann, and Jer-Shing [Gutachter] Hunag. "Novel applications of nanogratings for high resolution microscopy / Sapna Shukla Mukherjee ; Gutachter: Rainer Heintzmann, Jer-Shing Hunag." Jena : Friedrich-Schiller-Universität Jena, 2017. http://d-nb.info/1177595753/34.

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6

Denecker, Thomas. "Bioinformatique et analyse de données multiomiques : principes et applications chez les levures pathogènes Candida glabrata et Candida albicans Functional networks of co-expressed genes to explore iron homeostasis processes in the pathogenic yeast Candida glabrata Efficient, quick and easy-to-use DNA replication timing analysis with START-R suite FAIR_Bioinfo: a turnkey training course and protocol for reproducible computational biology Label-free quantitative proteomics in Candida yeast species: technical and biological replicates to assess data reproducibility Rendre ses projets R plus accessibles grâce à Shiny Pixel: a content management platform for quantitative omics data Empowering the detection of ChIP-seq "basic peaks" (bPeaks) in small eukaryotic genomes with a web user-interactive interface A hypothesis-driven approach identifies CDK4 and CDK6 inhibitors as candidate drugs for treatments of adrenocortical carcinomas Characterization of the replication timing program of 6 human model cell lines." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASL010.

Повний текст джерела
Анотація:
Plusieurs évolutions sont constatées dans la recherche en biologie. Tout d’abord, les études menées reposent souvent sur des approches expérimentales quantitatives. L’analyse et l’interprétation des résultats requièrent l’utilisation de l’informatique et des statistiques. Également, en complément des études centrées sur des objets biologiques isolés, les technologies expérimentales haut débit permettent l’étude des systèmes (caractérisation des composants du système ainsi que des interactions entre ces composants). De très grandes quantités de données sont disponibles dans les bases de données publiques, librement réutilisables pour de nouvelles problématiques. Enfin, les données utiles pour les recherches en biologie sont très hétérogènes (données numériques, de textes, images, séquences biologiques, etc.) et conservées sur des supports d’information également très hétérogènes (papiers ou numériques). Ainsi « l’analyse de données » s’est petit à petit imposée comme une problématique de recherche à part entière et en seulement une dizaine d’années, le domaine de la « Bioinformatique » s’est en conséquence totalement réinventé. Disposer d’une grande quantité de données pour répondre à un questionnement biologique n’est souvent pas le défi principal. La vraie difficulté est la capacité des chercheurs à convertir les données en information, puis en connaissance. Dans ce contexte, plusieurs problématiques de recherche en biologie ont été abordées lors de cette thèse. La première concerne l’étude de l’homéostasie du fer chez la levure pathogène Candida glabrata. La seconde concerne l’étude systématique des modifications post-traductionnelles des protéines chez la levure pathogène Candida albicans. Pour ces deux projets, des données « omiques » ont été exploitées : transcriptomiques et protéomiques. Des outils bioinformatiques et des outils d’analyses ont été implémentés en parallèle conduisant à l’émergence de nouvelles hypothèses de recherche en biologie. Une attention particulière et constante a aussi été portée sur les problématiques de reproductibilité et de partage des résultats avec la communauté scientifique
Biological research is changing. First, studies are often based on quantitative experimental approaches. The analysis and the interpretation of the obtained results thus need computer science and statistics. Also, together with studies focused on isolated biological objects, high throughput experimental technologies allow to capture the functioning of biological systems (identification of components as well as the interactions between them). Very large amounts of data are also available in public databases, freely reusable to solve new open questions. Finally, the data in biological research are heterogeneous (digital data, texts, images, biological sequences, etc.) and stored on multiple supports (paper or digital). Thus, "data analysis" has gradually emerged as a key research issue, and in only ten years, the field of "Bioinformatics" has been significantly changed. Having a large amount of data to answer a biological question is often not the main challenge. The real challenge is the ability of researchers to convert the data into information and then into knowledge. In this context, several biological research projects were addressed in this thesis. The first concerns the study of iron homeostasis in the pathogenic yeast Candida glabrata. The second concerns the systematic investigation of post-translational modifications of proteins in the pathogenic yeast Candida albicans. In these two projects, omics data were used: transcriptomics and proteomics. Appropriate bioinformatics and analysis tools were developed, leading to the emergence of new research hypotheses. Particular and constant attention has also been paid to the question of data reproducibility and sharing of results with the scientific community
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7

LEE, CHENG-WEI, and 李政葦. "An Interactive Web Application of Data Science with R using Shiny." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/469wk9.

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Анотація:
碩士
國立高雄大學
統計學研究所
107
This study uses the Shiny package in the R programming language to create an interactive web application for data science including time series, data mining, machine learning, regression analysis, etc. Since the Shiny package is a dynamic and interactive application that allows users to change settings and check results right away, it saves users a lot of time from rerunning and modifying programs. In addition, this interactive web application is very helpful for the introduction and further understanding of statistical methods. Users can describe the data through interactive methods and use R programming language to complete all operations, which is helpful for improving the effectiveness of data science in teaching and learning.
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8

Suleman, Nazira. "Mechanism-Based Approach to the Economic Evaluation of Pharmaceuticals." Master's thesis, 2018. http://hdl.handle.net/10451/40032.

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Анотація:
Tese de mestrado, Ciências Biofarmacêuticas, Universidade de Lisboa, Faculdade de Farmácia, 2018
A farmacoeconomia é uma disciplina que avalia o uso de medicamentos em termos de recursos na maximização da saúde da população. Dado que os recursos para os cuidados de saúde são finitos, a avaliação económica envolve a estimativa do custo de oportunidade, i.e., os benefícios marginais perdidos como resultado do deslocamento de tratamentos ou serviços existentes para financiar novos medicamentos. A farmacocinética é a ciência que visa o estudo do movimento de fármacos no organismo, o que inclui a absorção, distribuição, metabolismo e eliminação destes e seus metabolitos. Com o advento da química analítica e métodos de quantificação sofisticados, bem como de um aumento do poder de computação, a farmacocinética como ciência tem tido um desenvolvimento exponencial. Uma das áreas da farmacocinética que se tem desenvolvido mais é a farmacocinética populacional: apesar da farmacocinética de um fármaco poder ser estudada individualmente em cada indivíduo, a abordagem populacional é benéfica para o estudo de grupos de pacientes que são difíceis de investigar, como a população de bebés prematuros, pacientes com insuficiência hepática ou renal. Na farmacocinética populacional, cada indivíduo é avaliado simultaneamente com o modelo de efeitos mistos não-lineares (parametrização). Não linear significa que a variável dependente dessa concentração está relacionada não linearmente à associação de variáveis independentes e parâmetros do modelo. Efeitos fixos refere-se aos parâmetros que não se alteram em indivíduos, enquanto o efeito aleatório se refere àqueles parâmetros que se alteram através dos indivíduos. O principal objetivo das estimativas de modelação farmacocinética populacional é o de procurar os parâmetros de farmacocinética populacional e fonte de variabilidade. Os objetivos restantes consistem em concentrações observadas da dose administrada pela deteção das covariáveis preditivas na população avaliada. Em farmacocinética populacional, os indivíduos poderão apenas fornecer dados de concentração plasmática escassos. As cinco principais partes fundamentais para a construção de um modelo farmacocinético populacional incluem: dados, modelo estrutural, modelo estatístico, modelo de covariáveis e software de modelação. Os modelos estruturais definem o perfil de concentração plasmática ao longo do tempo nos indivíduos. Os modelos estatísticos descrevem a variabilidade aleatória na população que não é explicável (como a variabilidade entre as ocasiões), entre a variabilidade do indivíduo ou a variabilidade residual. Os modelos de covariável demonstram a variabilidade estimada pelas características da população, como covariáveis. O software de modelação, como o software de modelação de efeitos mistos não linear, permite a combinação de dados e modelos e aplica o método de estimativa para avaliar parâmetros para os modelos estatísticos, estruturais e de covariáveis que definem os dados. Na modelação farmacocinética populacional, o software possui um algoritmo de minimização do valor da função objetivo, praticando a estimativa de máxima verossimilhança. No momento da adaptação dos dados populacionais, a concentração estimada para cada indivíduo é influenciada pela variância dos parâmetros populacionais e de cada parâmetro individual, e pela variação em cada valor das concentrações previstas e observadas. A avaliação da probabilidade marginal depende dos parâmetros de efeito aleatório (η) e efeito fixo da população. Não há existência de solução analítica para verossimilhança marginal. Enquanto buscava a máxima verossimilhança, inúmeras abordagens foram aplicadas para a aproximação da verossimilhança marginal. O FOCE e o LAPLACE são as abordagens mais antigas que estimam a verdadeira verossimilhança com uma função adicional simplificada. O trabalho de dissertação no âmbito do Mestrado em Ciências Biofarmacêuticas teve por objetivo o estabelecimento de ferramentas baseadas em simulação de dados com base em modelos farmacocinéticos populacionais para uma posterior análise farmacoeconómica. Neste trabalho utilizou-se a informação disponível para a combinação fixa de Glecaprevir e Pibrentasvir (Mavyret®), medicamento usado no tratamento do vírus da hepatite C crónica. As simulações foram realizadas utilizando o software R e seu pacote Shiny. O R é uma linguagem para análise de dados de computação estatística e gráfica. A população simulada no modelo foi agrupada de acordo com as covariáveis similares, sendo simulados 1000 indivíduos por cenário. O relatório de submissão da FDA do Mavyret® foi usado como referência na modelação farmacocinética populacional. Neste relatório encontra-se descrito o modelo farmacocinético populacional desenvolvido, com base nos estudos clínicos realizados para o medicamento. No modelo descrito, foram identificadas diferentes covariáveis. O modelo descrito foi então implementado no software R e o impacto das covariáveis foi estudado com a aplicação Shiny. A população observada foi categorizada em diferentes grupos, tais como doentes tratados com Glecaprevir / Pibrentasvir com compromisso renal e doentes com compromisso renal e cirrose. Foram criados modelos individuais para cada um dos grupos e a comparação entre cada grupo e seus perfis de concentração-tempo foi realizada pelo uso do navegador R e Shiny, onde a atualização nos resultados pode ser vista automaticamente com a alteração em qualquer da covariável ou da variável. Para os diferentes modelos finais incorporados no software e para a população simulada, foram calculados os parâmetros farmacocinéticos AUC e Cmax para posterior análise estatística descritiva. Apesar da implementação dos modelos farmacocinéticos populacionais ter sido realizada em R e Shiny, e os dados terem sido simulados para os diferentes cenários populacionais, a aplicação de metodologias farmacoeconómicas não foram realizadas.
Pharmacoeconomics is the discipline concerned with optimal allocation of resources to maximize population health from the use of medicines. Given that resources for health care are finite, economic evaluation involves estimation of the opportunity cost, that is, the marginal benefits forgone as a result of displacing existing treatments or services to fund new medicines. The purpose of this study is to use tools in pharmacoeconomic analysis for the examination of the positive and adverse impact of the fixed dose combination of Glecaprevir and Pibrentasvir (Mavyret®), used to treat chronic hepatitis C virus. In order to examine the effects in pharmacoeconomics analysis, a population pharmacokinetic model was developed using R software and its package Shiny, where R is a language for data analysis of statistical computing and graphics. The population simulated in the model was grouped according to the similar covariates with the number (n) of 1000. FDA submission report for Mavyret® was used as reference regarding population pharmacokinetics modelling, developed based on the clinical studies performed for the drug product. In the described model, different covariates were identified. The described model was implemented in the R software and the impact of covariates wwas studied with Shiny application. The population observed was categorized in different groups such as patients treated with Glecaprevir/Pibrentasvir having renal impairment and patients with renal impairment and Cirrhosis. Individual models were created for each of the groups and the comparison between each group and their concentration-time profiles was observed that was made easier by the use of R and Shiny web browser where the update in results can be seen spontaneously with the change in any of the covariate or the variable. Different final models were produced and for the simulated population, the pharmacokinetic parameters AUC and Cmax were calculated for descriptive statistical analysis. Despite the implementation of population pharmacokinetics models has been accomplished in R and Shiny, and data has been simulated for different population scenarios, pharmacoeconomic modelling and application of pharmacoeconomic methodologies was not practised.
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Chen, Szu-Chuan, and 陳思全. "Green villageSynbiotic eco-village planning－An Application for Jubei and Chiunglin City Shin-Chu county." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/99539516183390894640.

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Анотація:
碩士
中原大學
建築研究所
90
After the industry revolution ,the human being always hold a concept of environment that man will triumph over nature for a long time .And it makes the main stream value of economic trading system keep overriding the care of ecological environment. In Taiwan,our economics grows rapidly and the industrial business property are developed vigorous from the beginning of R.O.C.. Under the background of the whole times ,the changes of ecological environment also replaced from nature to artificial, and developed behavior replaced the current environment ,the pounce on nature again after being beaten off step by step .People still haven’t be awaken by the importance of paragenesis ecological until now. The research is focus on the 1250 hectare land development case of Pu-Yu plan and the community planner system in Hsin-Chu county. They use the concept of gather village to do a hypothesize operation in specified extent and they try to find a starting point from the original power of community construction to connect with large scale of work out system. Different from the past government plan assignment, they expect to build a plan system based on the life of resident and paragenesis ecological under the current background construction. The research background are mainly divided into three premise of research to do a deep analysis : residential life, paragenesis ecological and the truely locality. The first part of research contents is using the research one himself to record local ”people” pattern in practical participated observation way and try to provide the imagination of large scale plan construction to build the follow-up operation system .The second part is using the hypothesized plan way to operate the countryside land by the station of current Pu-Yu plan. Furthermore, they use the community operation mode by community planner to discuss the real practical possibilities. The final submission part is bring tactical operation method into the whole plan and develop the ecological environment value of new era emphatically ,creating a paragenesis ecological by nature ecology and humane social system.
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LIN, YUAN-WEN, and 林淵文. "The application of multivariate statistical method in water quality for the basin of shin-men reservoir." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/24370936631239640637.

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Книги з теми "Application Shiny"

1

Ankrah, Stephanie. Protective materials for sporting applications - football shin guards. Birmingham: University of Birmingham, 2002.

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Resnizky, Hernan G. Learning Shiny. Packt Publishing, 2015.

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Web Application Development with R Using Shiny. Packt Publishing, 2013.

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Web Application Development with R Using Shiny. Packt Publishing, Limited, 2013.

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Web Application Development with R Using Shiny - Second Edition: Integrate the power of R with the simplicity of Shiny to deliver cutting-edge analytics over the Web. Packt Publishing, 2016.

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Toolkit for Weighting and Analysis of Nonequivalent Groups: A Tutorial on the TWANG Shiny Application for Time-Varying Treatments. RAND Corporation, 2021. http://dx.doi.org/10.7249/tla570-4.

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Griffin, Beth Ann, Chuck Stelzner, Ricardo Sanchez, Matthew Cefalu, and Daniel McCaffrey. Toolkit for Weighting and Analysis of Nonequivalent Groups: A Tutorial on the TWANG Shiny Application for Three or More Treatment Groups. RAND Corporation, 2020. http://dx.doi.org/10.7249/tla570-1.

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Beeley, Chris, and Shitalkumar R. Sukhdeve. Web Application Development with R Using Shiny: Build stunning graphics and interactive data visualizations to deliver cutting-edge analytics, 3rd Edition. Packt Publishing, 2018.

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Engineering Production-Grade Shiny Apps. Taylor & Francis Group, 2021.

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Fay, Colin, S�bastien Rochette, Vincent Guyader, and Cervan Girard. Engineering Production-Grade Shiny Apps. Taylor & Francis Group, 2021.

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Частини книг з теми "Application Shiny"

1

Granjon, David. "Web application concepts." In Outstanding User Interfaces with Shiny, 65–75. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003124924-5.

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Fay, Colin, Sébastien Rochette, Vincent Guyader, and Cervan Girard. "Common Application Caveats." In Engineering Production-Grade Shiny Apps, 239–66. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003029878-22.

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Fay, Colin, Sébastien Rochette, Vincent Guyader, and Cervan Girard. "Deploy Your Application." In Engineering Production-Grade Shiny Apps, 207–12. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003029878-19.

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Alves, Samuel, Ana Cristina Braga, and Rosete Nogueira. "Percentile Growth Curves for Placenta Measures: A Dynamic Shiny Application." In Computational Science and Its Applications – ICCSA 2022 Workshops, 543–54. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10536-4_36.

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Albert, Jim, Max Marchi, and Benjamin S. Baumer. "Using Shiny for Baseball Applications." In Analyzing Baseball Data with R, 334–47. 3rd ed. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781032668239-15.

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Baker, Katie, Gordon Stephen, Shona Strachan, Miles Armstrong, and Ingo Hein. "BLASTmap: A Shiny-Based Application to Visualize BLAST Results as Interactive Heat Maps and a Tool to Design Gene-Specific Baits for Bespoke Target Enrichment Sequencing." In Methods in Molecular Biology, 199–206. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8724-5_14.

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Antunes, Ana Rita, and Ana Cristina Braga. "Shiny App to Predict Agricultural Tire Dimensions." In Computational Science and Its Applications – ICCSA 2020, 247–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58808-3_19.

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Liu, Junjie, Yuhui Deng, and Xiaoling Peng. "Online Statistics Teaching-Assisted Platform with Interactive Web Applications Using R Shiny." In Emerging Technologies for Education, 84–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92836-0_8.

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Yoshida, Sen, Koji Kamei, Takeshi Ohguro, Kazuhiro Kuwabara, and Kaname Funakoshi. "Building a Network Community Support System on the Multi-agent Platform Shine." In Design and Applications of Intelligent Agents, 88–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44594-3_7.

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Rodrigues, Claudia, Ana Rita Antunes, and Ana Cristina Braga. "Shiny App to Predict the Risk of Death in Very Low Birth Weight Newborns Through a New Classifier." In Computational Science and Its Applications – ICCSA 2021, 593–608. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86973-1_42.

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Тези доповідей конференцій з теми "Application Shiny"

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Anggono, W. S. P. Dwi, Kristoko Dwi Hartomo, and Eko Sediyono. "Standardized precipitation index web application mapping shiny model." In 2017 International Conference on Innovative and Creative Information Technology (ICITech). IEEE, 2017. http://dx.doi.org/10.1109/innocit.2017.8319131.

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Satyahadewi, Neva, and Hendra Perdana. "Web Application Development for Inferential Statistics using R Shiny." In 1st International Conference on Mathematics and Mathematics Education (ICMMEd 2020). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.210508.099.

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Scrivner, Olga, Vinita Chakilam, Nilima Sahoo, and Stephan De Spiegeleire. "Topic Analysis through Streamgraph via Shiny Application: A Social Collaborative Approach." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2018. http://dx.doi.org/10.24251/hicss.2018.025.

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Konda, Kazuki, and Yoshiro Yamamoto. "Analyze the trends of customer purchase data and visualize by the Shiny application." In 2018 16th International Conference on ICT and Knowledge Engineering (ICT&KE). IEEE, 2018. http://dx.doi.org/10.1109/ictke.2018.8612310.

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Bani, Ryota, and Yoshiro Yamamoto. "Development of an R-Shiny-based Shooting Area Visualization Application for Use in Basketball." In 2022 20th International Conference on ICT and Knowledge Engineering (ICT&KE). IEEE, 2022. http://dx.doi.org/10.1109/ictke55848.2022.9983260.

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Murillo, Danny, Dalys Saavedra, and Robinson Zapata. "Web application in Shiny for the extraction of data from profiles in Google Scholar." In 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research and Leadership in Post-pandemic Engineering: Resilient, Inclusive and Sustainable Actions”. Latin American and Caribbean Consortium of Engineering Institutions, 2022. http://dx.doi.org/10.18687/laccei2022.1.1.235.

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Harding, Kevin G. "Measuring shiny surfaces with optical gages." In Dimensional Optical Metrology and Inspection for Practical Applications XIII, edited by Song Zhang, Kevin G. Harding, Andrés G. Marrugo, Beiwen Li, and Jae-Sang Hyun. SPIE, 2024. http://dx.doi.org/10.1117/12.3013004.

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Zharri, Elona, and Stefan A. Robila. "Shiny Dashboard - NYC Trees Benefit Estimation." In 2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT). IEEE, 2022. http://dx.doi.org/10.1109/lisat50122.2022.9923953.

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Crossett, Jonathan, Jing Qi, William Cowen, Elijah Gagne, and Christian Darabos. "Shiny Concurrency: Scaling Single-Threaded and Resource Intensive Applications." In PEARC '24: Practice and Experience in Advanced Research Computing. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3626203.3670590.

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10

Morgan, Jeffrey J., Otto C. Wilson, and Prahlad G. Menon. "The Wisdom of Crowds Approach to Influenza-Rate Forecasting." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86559.

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Influenza is an important public health concern. Influenza leads to the death or hospitalization of thousands of people around the globe every year. However, the flu-season varies every year viz. when it starts, when it peaks, and the severity of the outbreak. Knowing the trajectory of the epidemic outbreak is important for taking appropriate mitigation strategies. Starting with the 2013–2014 flu season, the Influenza Division of the Centers for Disease Control and Prevention (CDC) has held a “Predict the Influenza Season Challenge” to encourage the scientific community to make advances in the field of influenza forecasting. A key observation from these challenges is that a simple average of the submitted forecasts outperformed nearly all of the individual models. Further, ongoing efforts seek ways to assign weights to individual models to create high-performing ensemble models. Given the sheer number of models, as well as variation in methodology followed among teams contributing influenza-risk forecasts, multiple forecasting models can be combined, by capturing human judgment, to outperform a simple average of these same models. This project exploits such a “wisdom of crowds” approach, using public votes acquired with the help of an R/Shiny based web-application platform in order to assign weights to individual forecasting models on a week-over-week basis, in an effort to improve overall ILI risk prediction accuracy. We describe a strategy for improving the accuracy of influenza risk forecast modeling based on a crowd-sourced set of team-specific forecast votes and the results of the 2017–2018 season. Our approach to assigning weights based on crowd-sourced votes on individual models outperformed an average forecasts of the individual models. The crowd was statistically significantly more accurate than the average model and all but one of the individual models.
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Звіти організацій з теми "Application Shiny"

1

Mazorchuk, Mariia S., Tetyana S. Vakulenko, Anna O. Bychko, Olena H. Kuzminska, and Oleksandr V. Prokhorov. Cloud technologies and learning analytics: web application for PISA results analysis and visualization. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4451.

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This article analyzes the ways to apply Learning Analytics, Cloud Technologies, and Big Data in the field of education on the international level. This paper provides examples of international analytical researches and cloud technologies used to process the results of those researches. It considers the PISA research methodology and related tools, including the IDB Analyzer application, free R intsvy environment for processing statistical data, and cloud-based web application PISA Data Explorer. The paper justifies the necessity of creating a stand-alone web application that supports Ukrainian localization and provides Ukrainian researchers with rapid access to well-structured PISA data. In particular, such an application should provide for data across the factorial features and indicators applied at the country level and demonstrate the Ukrainian indicators compared to the other countries’ results. This paper includes a description of the application core functionalities, architecture, and technologies used for development. The proposed solution leverages the shiny package available with R environment that allows implementing both the UI and server sides of the application. The technical implementation is a proven solution that allows for simplifying the access to PISA data for Ukrainian researchers and helping them utilize the calculation results on the key features without having to apply tools for processing statistical data.
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Armstrong, Dave. Interactive Dashboards and Web Apps with Shiny in R. Instats Inc., 2023. http://dx.doi.org/10.61700/3g41zs4gcnd3e469.

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Анотація:
This seminar introduces Shiny, the R package that serves as a framework for building interactive web applications. One of the benefits of Shiny is that it allows dynamic cloud calculations that you can integrate into your research project (e.g., running statistical models with a custom specification, filtering and summarizing data, producing custom graphics). This seminar discusses how to build Shiny apps, how to customize their appearance, and how to host them so other users can view and interact with them -- readers, reviewers, or other research team members. An official Instats certificate of completion is provided at the conclusion of the seminar. The seminar offers 2 ECTS Equivalent points for European PhD students.
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Armstrong, Dave. Interactive Dashboards and Web Apps with Shiny in R. Instats Inc., 2023. http://dx.doi.org/10.61700/b3vv6dox5eqdl469.

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Анотація:
This seminar introduces Shiny, the R package that serves as a framework for building interactive web applications. One of the benefits of Shiny is that it allows dynamic cloud calculations that you can integrate into your research project (e.g., running statistical models with a custom specification, filtering and summarizing data, producing custom graphics). This seminar discusses how to build Shiny apps, how to customize their appearance, and how to host them so other users can view and interact with them -- readers, reviewers, or other research team members. An official Instats certificate of completion is provided at the conclusion of the seminar. The seminar offers 2 ECTS Equivalent points for European PhD students.
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Ihaddaden, Fodil. Introduction to Web App Development with R Shiny. Instats Inc., 2024. http://dx.doi.org/10.61700/c3vssh3mivcmo1636.

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Анотація:
This workshop provides an extensive introduction to developing interactive web applications using R Shiny, enabling participants to create, deploy, and optimize data-driven web tools. Ideal for PhD students, professors, and researchers, the course covers fundamental and advanced concepts tailored to enhance research presentation and real-time analytical communication across various scientific domains.
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