The Experts below are selected from a list of 73374 Experts worldwide ranked by ideXlab platform
Hans Petter Langtangen - One of the best experts on this subject based on the ideXlab platform.
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chaospy an Open Source Tool for designing methods of uncertainty quantification
Journal of Computational Science, 2015Co-Authors: Jonathan Feinberg, Hans Petter LangtangenAbstract:Abstract The paper describes the philosophy, design, functionality, and usage of the Python software Toolbox Chaospy for performing uncertainty quantification via polynomial chaos expansions and Monte Carlo simulation. The paper compares Chaospy to similar packages and demonstrates a stronger focus on defining reusable software building blocks that can easily be assembled to construct new, tailored algorithms for uncertainty quantification. For example, a Chaospy user can in a few lines of high-level computer code define custom distributions, polynomials, integration rules, sampling schemes, and statistical metrics for uncertainty analysis. In addition, the software introduces some novel methodological advances, like a framework for computing Rosenblatt transformations and a new approach for creating polynomial chaos expansions with dependent stochastic variables.
Jeffrey D Bradley - One of the best experts on this subject based on the ideXlab platform.
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dose response explorer an integrated Open Source Tool for exploring and modelling radiotherapy dose volume outcome relationships
Physics in Medicine and Biology, 2006Co-Authors: El I Naqa, Gita Suneja, P Lindsay, Andrew J Hope, J Alaly, M Vicic, Jeffrey D BradleyAbstract:Radiotherapy treatment outcome models are a complicated function of treatment, clinical and biological factors. Our objective is to provide clinicians and scientists with an accurate, flexible and user-friendly software Tool to explore radiotherapy outcomes data and build statistical tumour control or normal tissue complications models. The software Tool, called the dose response explorer system (DREES), is based on Matlab, and uses a named-field structure array data type. DREES/Matlab in combination with another Open-Source Tool (CERR) provides an environment for analysing treatment outcomes. DREES provides many radiotherapy outcome modelling features, including (1) fitting of analytical normal tissue complication probability (NTCP) and tumour control probability (TCP) models, (2) combined modelling of multiple dose-volume variables (e.g., mean dose, max dose, etc) and clinical factors (age, gender, stage, etc) using multi-term regression modelling, (3) manual or automated selection of logistic or actuarial model variables using bootstrap statistical resampling, (4) estimation of uncertainty in model parameters, (5) performance assessment of univariate and multivariate analyses using Spearman's rank correlation and chi-square statistics, boxplots, nomograms, Kaplan-Meier survival plots, and receiver operating characteristics curves, and (6) graphical capabilities to visualize NTCP or TCP prediction versus selected variable models using various plots. DREES provides clinical researchers with a Tool customized for radiotherapy outcome modelling. DREES is freely distributed. We expect to continue developing DREES based on user feedback.
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Dose response explorer: an integrated Open-Source Tool for exploring and modelling radiotherapy dose–volume outcome relationships
Physics in Medicine and Biology, 2006Co-Authors: I. El Naqa, Gita Suneja, Andrew J Hope, J Alaly, M Vicic, Jeffrey D Bradley, P.e. Lindsay, Aditya Apte, Joseph O. DeasyAbstract:Radiotherapy treatment outcome models are a complicated function of treatment, clinical and biological factors. Our objective is to provide clinicians and scientists with an accurate, flexible and user-friendly software Tool to explore radiotherapy outcomes data and build statistical tumour control or normal tissue complications models. The software Tool, called the dose response explorer system (DREES), is based on Matlab, and uses a named-field structure array data type. DREES/Matlab in combination with another Open-Source Tool (CERR) provides an environment for analysing treatment outcomes. DREES provides many radiotherapy outcome modelling features, including (1) fitting of analytical normal tissue complication probability (NTCP) and tumour control probability (TCP) models, (2) combined modelling of multiple dose–volume variables (e.g., mean dose, max dose, etc) and clinical factors (age, gender, stage, etc) using multi-term regression modelling, (3) manual or automated selection of logistic or actuarial model variables using bootstrap statistical resampling, (4) estimation of uncertainty in model parameters, (5) performance assessment of univariate and multivariate analyses using Spearman's rank correlation and chi-square statistics, boxplots, nomograms, Kaplan–Meier survival plots, and receiver operating characteristics curves, and (6) graphical capabilities to visualize NTCP or TCP prediction versus selected variable models using various plots. DREES provides clinical researchers with a Tool customized for radiotherapy outcome modelling. DREES is freely distributed. We expect to continue developing DREES based on user feedback. For more information on this article, see medicalphysicsweb.org
James A Hay - One of the best experts on this subject based on the ideXlab platform.
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an Open Source Tool to infer epidemiological and immunological dynamics from serological data serosolver
PLOS Computational Biology, 2020Co-Authors: James A Hay, Amanda Minter, Kylie E C Ainslie, Justin Lessler, Bingyi Yang, Derek A T Cummings, Adam J Kucharski, Steven RileyAbstract:We present a flexible, Open Source R package designed to obtain biological and epidemiological insights from serological datasets. Characterising past exposures for multi-strain pathogens poses a specific statistical challenge: observed antibody responses measured in serological assays depend on multiple unobserved prior infections that produce cross-reactive antibody responses. We provide a general modelling framework to jointly infer infection histories and describe immune responses generated by these infections using antibody titres against current and historical strains. We do this by linking latent infection dynamics with a mechanistic model of antibody kinetics that generates expected antibody titres over time. Our aim is to provide a flexible package to identify infection histories that can be applied to a range of pathogens. We present two case studies to illustrate how our model can infer key immunological parameters, such as antibody titre boosting, waning and cross-reaction, as well as latent epidemiological processes such as attack rates and age-stratified infection risk.
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serosolver an Open Source Tool to infer epidemiological and immunological dynamics from serological data
bioRxiv, 2019Co-Authors: James A Hay, Amanda Minter, Kylie E C Ainslie, Justin Lessler, Adam J Kucharski, Steven RileyAbstract:Abstract We present a flexible, Open Source R package designed to obtain additional biological and epidemiological insights from commonly available serological datasets. Analysis of serological responses against pathogens with multiple strains such as influenza pose a specific statistical challenge because observed antibody responses measured in serological assays depend both on unobserved prior infections and the resulting cross-reactive antibody dynamics that these infections generate. We provide a general modelling framework to jointly infer these two typically confounded biological processes using antibody titres against current and historical strains. We do this by linking latent infection dynamics with a mechanistic model of antibody dynamics that generates expected antibody titres over time. This makes it possible to use observations of antibodies in serological assays to infer an individual’s infection history as well as the parameters of the antibody process model. Our aim is to provide a flexible inference package that can be applied to a range of datasets studying different viruses over different timescales. We present two case studies to illustrate how our model can infer key immunological parameters, such as antibody titre boosting, waning and cross-reaction, and well as latent epidemiological processes such as attack rates and age-stratified infection risk.
M Vicic - One of the best experts on this subject based on the ideXlab platform.
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dose response explorer an integrated Open Source Tool for exploring and modelling radiotherapy dose volume outcome relationships
Physics in Medicine and Biology, 2006Co-Authors: El I Naqa, Gita Suneja, P Lindsay, Andrew J Hope, J Alaly, M Vicic, Jeffrey D BradleyAbstract:Radiotherapy treatment outcome models are a complicated function of treatment, clinical and biological factors. Our objective is to provide clinicians and scientists with an accurate, flexible and user-friendly software Tool to explore radiotherapy outcomes data and build statistical tumour control or normal tissue complications models. The software Tool, called the dose response explorer system (DREES), is based on Matlab, and uses a named-field structure array data type. DREES/Matlab in combination with another Open-Source Tool (CERR) provides an environment for analysing treatment outcomes. DREES provides many radiotherapy outcome modelling features, including (1) fitting of analytical normal tissue complication probability (NTCP) and tumour control probability (TCP) models, (2) combined modelling of multiple dose-volume variables (e.g., mean dose, max dose, etc) and clinical factors (age, gender, stage, etc) using multi-term regression modelling, (3) manual or automated selection of logistic or actuarial model variables using bootstrap statistical resampling, (4) estimation of uncertainty in model parameters, (5) performance assessment of univariate and multivariate analyses using Spearman's rank correlation and chi-square statistics, boxplots, nomograms, Kaplan-Meier survival plots, and receiver operating characteristics curves, and (6) graphical capabilities to visualize NTCP or TCP prediction versus selected variable models using various plots. DREES provides clinical researchers with a Tool customized for radiotherapy outcome modelling. DREES is freely distributed. We expect to continue developing DREES based on user feedback.
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Dose response explorer: an integrated Open-Source Tool for exploring and modelling radiotherapy dose–volume outcome relationships
Physics in Medicine and Biology, 2006Co-Authors: I. El Naqa, Gita Suneja, Andrew J Hope, J Alaly, M Vicic, Jeffrey D Bradley, P.e. Lindsay, Aditya Apte, Joseph O. DeasyAbstract:Radiotherapy treatment outcome models are a complicated function of treatment, clinical and biological factors. Our objective is to provide clinicians and scientists with an accurate, flexible and user-friendly software Tool to explore radiotherapy outcomes data and build statistical tumour control or normal tissue complications models. The software Tool, called the dose response explorer system (DREES), is based on Matlab, and uses a named-field structure array data type. DREES/Matlab in combination with another Open-Source Tool (CERR) provides an environment for analysing treatment outcomes. DREES provides many radiotherapy outcome modelling features, including (1) fitting of analytical normal tissue complication probability (NTCP) and tumour control probability (TCP) models, (2) combined modelling of multiple dose–volume variables (e.g., mean dose, max dose, etc) and clinical factors (age, gender, stage, etc) using multi-term regression modelling, (3) manual or automated selection of logistic or actuarial model variables using bootstrap statistical resampling, (4) estimation of uncertainty in model parameters, (5) performance assessment of univariate and multivariate analyses using Spearman's rank correlation and chi-square statistics, boxplots, nomograms, Kaplan–Meier survival plots, and receiver operating characteristics curves, and (6) graphical capabilities to visualize NTCP or TCP prediction versus selected variable models using various plots. DREES provides clinical researchers with a Tool customized for radiotherapy outcome modelling. DREES is freely distributed. We expect to continue developing DREES based on user feedback. For more information on this article, see medicalphysicsweb.org
J Alaly - One of the best experts on this subject based on the ideXlab platform.
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dose response explorer an integrated Open Source Tool for exploring and modelling radiotherapy dose volume outcome relationships
Physics in Medicine and Biology, 2006Co-Authors: El I Naqa, Gita Suneja, P Lindsay, Andrew J Hope, J Alaly, M Vicic, Jeffrey D BradleyAbstract:Radiotherapy treatment outcome models are a complicated function of treatment, clinical and biological factors. Our objective is to provide clinicians and scientists with an accurate, flexible and user-friendly software Tool to explore radiotherapy outcomes data and build statistical tumour control or normal tissue complications models. The software Tool, called the dose response explorer system (DREES), is based on Matlab, and uses a named-field structure array data type. DREES/Matlab in combination with another Open-Source Tool (CERR) provides an environment for analysing treatment outcomes. DREES provides many radiotherapy outcome modelling features, including (1) fitting of analytical normal tissue complication probability (NTCP) and tumour control probability (TCP) models, (2) combined modelling of multiple dose-volume variables (e.g., mean dose, max dose, etc) and clinical factors (age, gender, stage, etc) using multi-term regression modelling, (3) manual or automated selection of logistic or actuarial model variables using bootstrap statistical resampling, (4) estimation of uncertainty in model parameters, (5) performance assessment of univariate and multivariate analyses using Spearman's rank correlation and chi-square statistics, boxplots, nomograms, Kaplan-Meier survival plots, and receiver operating characteristics curves, and (6) graphical capabilities to visualize NTCP or TCP prediction versus selected variable models using various plots. DREES provides clinical researchers with a Tool customized for radiotherapy outcome modelling. DREES is freely distributed. We expect to continue developing DREES based on user feedback.
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Dose response explorer: an integrated Open-Source Tool for exploring and modelling radiotherapy dose–volume outcome relationships
Physics in Medicine and Biology, 2006Co-Authors: I. El Naqa, Gita Suneja, Andrew J Hope, J Alaly, M Vicic, Jeffrey D Bradley, P.e. Lindsay, Aditya Apte, Joseph O. DeasyAbstract:Radiotherapy treatment outcome models are a complicated function of treatment, clinical and biological factors. Our objective is to provide clinicians and scientists with an accurate, flexible and user-friendly software Tool to explore radiotherapy outcomes data and build statistical tumour control or normal tissue complications models. The software Tool, called the dose response explorer system (DREES), is based on Matlab, and uses a named-field structure array data type. DREES/Matlab in combination with another Open-Source Tool (CERR) provides an environment for analysing treatment outcomes. DREES provides many radiotherapy outcome modelling features, including (1) fitting of analytical normal tissue complication probability (NTCP) and tumour control probability (TCP) models, (2) combined modelling of multiple dose–volume variables (e.g., mean dose, max dose, etc) and clinical factors (age, gender, stage, etc) using multi-term regression modelling, (3) manual or automated selection of logistic or actuarial model variables using bootstrap statistical resampling, (4) estimation of uncertainty in model parameters, (5) performance assessment of univariate and multivariate analyses using Spearman's rank correlation and chi-square statistics, boxplots, nomograms, Kaplan–Meier survival plots, and receiver operating characteristics curves, and (6) graphical capabilities to visualize NTCP or TCP prediction versus selected variable models using various plots. DREES provides clinical researchers with a Tool customized for radiotherapy outcome modelling. DREES is freely distributed. We expect to continue developing DREES based on user feedback. For more information on this article, see medicalphysicsweb.org
