scholarly journals eTOXlab, an open source modeling framework for implementing predictive models in production environments

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Pau Carrió ◽  
Oriol López ◽  
Ferran Sanz ◽  
Manuel Pastor
2019 ◽  
Vol 15 (10) ◽  
pp. 1309-1321 ◽  
Author(s):  
Colin Green ◽  
Ron Handels ◽  
Anders Gustavsson ◽  
Anders Wimo ◽  
Bengt Winblad ◽  
...  

2018 ◽  
Author(s):  
Jan Philipp Dietrich ◽  
Benjamin Leon Bodirsky ◽  
Florian Humpenöder ◽  
Isabelle Weindl ◽  
Miodrag Stevanović ◽  
...  

Abstract. The open source modeling framework MAgPIE combines economic and biophysical approaches to simulate spatially-explicit global scenarios of landuse within the 21st century and the respective interactions with the environment. Besides various other projects, it was used to simulate marker scenarios of the Shared Socio-economic Pathways (SSPs) and contributed substantially to multiple IPCC assessments. However, with growing scope and detail, the non-linear model has become increasingly complex, computational intensive, and intransparent, requiring structured approaches to improve the development and evaluation of the model. Here we provide an overview on version 4 of MAgPIE, and how it addresses these issues of increasing complexity using new technical features: modular structure, flexible detail in process dynamics, flexible spatial resolution, in-code documentation, automatized code-checking, model/output evaluation, and open accessibility. Application examples provide insights into model evaluation and region-specific analysis approaches. While this paper is focused on the general framework as such, the publication is accompanied by a detailed model documentation describing contents and equations, and by model evaluation documents giving insights into model performance for a broad range of variables. With the open source release of the MAgPIE 4 framework we hope to contribute to more transparent, reproducible and collaborative research in the field. Due to its modularity and spatial flexibility it should provide a basis for a broad range of land-related research with economic or biophysical, global or regional focus.


2019 ◽  
Vol 15 (7) ◽  
pp. P1636
Author(s):  
Colin Green ◽  
Ron Handels ◽  
Anders Gustavsson ◽  
Anders Wimo ◽  
Anders Skoldunger ◽  
...  

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Manuel Pastor ◽  
José Carlos Gómez-Tamayo ◽  
Ferran Sanz

AbstractThis article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application with a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures since the software supports structural normalization, molecular descriptor calculation, and machine learning model generation using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code, adding unlimited model customization. Models generated with Flame can be easily exported, facilitating collaborative model development. All models are stored in a model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly 3 years of development in collaboration with users from the pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high-quality predictive models based on shared legacy data for assessing the safety of drug candidates.


2015 ◽  
Vol 68 ◽  
pp. 205-218 ◽  
Author(s):  
A. Sen Gupta ◽  
D.G. Tarboton ◽  
P. Hummel ◽  
M.E. Brown ◽  
S. Habib

2019 ◽  
Vol 12 (4) ◽  
pp. 1299-1317 ◽  
Author(s):  
Jan Philipp Dietrich ◽  
Benjamin Leon Bodirsky ◽  
Florian Humpenöder ◽  
Isabelle Weindl ◽  
Miodrag Stevanović ◽  
...  

Abstract. The open-source modeling framework MAgPIE (Model of Agricultural Production and its Impact on the Environment) combines economic and biophysical approaches to simulate spatially explicit global scenarios of land use within the 21st century and the respective interactions with the environment. Besides various other projects, it was used to simulate marker scenarios of the Shared Socioeconomic Pathways (SSPs) and contributed substantially to multiple IPCC assessments. However, with growing scope and detail, the non-linear model has become increasingly complex, computationally intensive and non-transparent, requiring structured approaches to improve the development and evaluation of the model.Here, we provide an overview on version 4 of MAgPIE and how it addresses these issues of increasing complexity using new technical features: modular structure with exchangeable module implementations, flexible spatial resolution, in-code documentation, automatized code checking, model/output evaluation and open accessibility. Application examples provide insights into model evaluation, modular flexibility and region-specific analysis approaches. While this paper is focused on the general framework as such, the publication is accompanied by a detailed model documentation describing contents and equations, and by model evaluation documents giving insights into model performance for a broad range of variables.With the open-source release of the MAgPIE 4 framework, we hope to contribute to more transparent, reproducible and collaborative research in the field. Due to its modularity and spatial flexibility, it should provide a basis for a broad range of land-related research with economic or biophysical, global or regional focus.


2020 ◽  
Author(s):  
Manuel Pastor ◽  
José Carlos Gómez-Tamayo ◽  
Ferran Sanz

Abstract This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application, with a Python backend and a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures, since the software supports structural normalization, molecular descriptor generation and machine learning building, using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code adding unlimited model customization. Models generated with Flame can be easily exported facilitating collaborative model development. All models are stored in a persistent model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly three years of development in collaboration with users from pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high quality predictive models based on shared legacy data for assessing the safety of drug candidates.


2014 ◽  
Vol 28 (S1) ◽  
Author(s):  
James Bassingthwaighte ◽  
Erik Butterworth ◽  
Bart Jardine ◽  
Gary Raymond ◽  
Maxwell Neal

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