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Anžel A, Heider D, Hattab G. Interactive polar diagrams for model comparison. Comput Methods Programs Biomed 2023; 242:107843. [PMID: 37832432 DOI: 10.1016/j.cmpb.2023.107843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 09/16/2023] [Accepted: 10/02/2023] [Indexed: 10/15/2023]
Abstract
OBJECTIVE Evaluating the performance of multiple complex models, such as those found in biology, medicine, climatology, and machine learning, using conventional approaches is often challenging when using various evaluation metrics simultaneously. The traditional approach, which relies on presenting multi-model evaluation scores in the table, presents an obstacle when determining the similarities between the models and the order of performance. METHODS By combining statistics, information theory, and data visualization, juxtaposed Taylor and Mutual Information Diagrams permit users to track and summarize the performance of one model or a collection of different models. To uncover linear and nonlinear relationships between models, users may visualize one or both charts. RESULTS Our library presents the first publicly available implementation of the Mutual Information Diagram and its new interactive capabilities, as well as the first publicly available implementation of an interactive Taylor Diagram. Extensions have been implemented so that both diagrams can display temporality, multimodality, and multivariate data sets, and feature one scalar model property such as uncertainty. Our library, named polar-diagrams, supports both continuous and categorical attributes. CONCLUSION The library can be used to quickly and easily assess the performances of complex models, such as those found in machine learning, climate, or biomedical domains.
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Affiliation(s)
- Aleksandar Anžel
- Department of Mathematics & Computer Science, University of Marburg, Hans-Meerwein-Straße 6, Marburg, D-35032, Hesse, Germany.
| | - Dominik Heider
- Department of Mathematics & Computer Science, University of Marburg, Hans-Meerwein-Straße 6, Marburg, D-35032, Hesse, Germany
| | - Georges Hattab
- Center for Artificial Intelligence in Public Health Research (ZKI-PH), Robert Koch-Institute, Nordufer 20, Berlin, 13353, Berlin, Germany; Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 14, Berlin, 14195, Berlin, Germany
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Raubach S, Schreiber M, Shaw PD. GridScore: a tool for accurate, cross-platform phenotypic data collection and visualization. BMC Bioinformatics 2022; 23:214. [PMID: 35668357 PMCID: PMC9169276 DOI: 10.1186/s12859-022-04755-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 05/30/2022] [Indexed: 11/17/2022] Open
Abstract
Background Plant breeding and crop research rely on experimental phenotyping trials. These trials generate data for large numbers of traits and plant varieties that needs to be captured efficiently and accurately to support further research and downstream analysis. Traditionally scored by hand, phenotypic data is nowadays collected using spreadsheets or specialized apps. While many solutions exist, which increase efficiency and reduce errors, none offer the same familiarity as printed field plans which have been used for decades and offer an intuitive overview over the trial setup, previously recorded data and plots still requiring scoring. Results We introduce GridScore which utilizes cutting-edge web technologies to reproduce the familiarity of printed field plans while enhancing the phenotypic data collection process by adding advanced features like georeferencing, image tagging and speech recognition. GridScore is a cross-platform open-source plant phenotyping app that combines barcode-based systems with a guided data collection approach while offering a top-down view onto the data collected in a field layout. GridScore is compared to existing tools across a wide spectrum of criteria including support for barcodes, multiple platforms, and visualizations. Conclusion Compared to its competition, GridScore shows strong performance across the board offering a complete manual phenotyping experience.
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Affiliation(s)
- Sebastian Raubach
- Department of Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee, Scotland.
| | - Miriam Schreiber
- Department of Life Science, University of Dundee, Dundee, Scotland
| | - Paul D Shaw
- Department of Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee, Scotland
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P'ng C, Green J, Chong LC, Waggott D, Prokopec SD, Shamsi M, Nguyen F, Mak DYF, Lam F, Albuquerque MA, Wu Y, Jung EH, Starmans MHW, Chan-Seng-Yue MA, Yao CQ, Liang B, Lalonde E, Haider S, Simone NA, Sendorek D, Chu KC, Moon NC, Fox NS, Grzadkowski MR, Harding NJ, Fung C, Murdoch AR, Houlahan KE, Wang J, Garcia DR, de Borja R, Sun RX, Lin X, Chen GM, Lu A, Shiah YJ, Zia A, Kearns R, Boutros PC. BPG: Seamless, automated and interactive visualization of scientific data. BMC Bioinformatics 2019; 20:42. [PMID: 30665349 PMCID: PMC6341661 DOI: 10.1186/s12859-019-2610-2] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 01/04/2019] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND We introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment. RESULTS This open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines. CONCLUSION BPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.general.
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Affiliation(s)
| | - Jeffrey Green
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | - Daryl Waggott
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | | | | | | | - Felix Lam
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | - Ying Wu
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Esther H Jung
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | | | - Cindy Q Yao
- Ontario Institute for Cancer Research, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Bianca Liang
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Emilie Lalonde
- Ontario Institute for Cancer Research, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Syed Haider
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | | | - Kenneth C Chu
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | - Natalie S Fox
- Ontario Institute for Cancer Research, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | | | | | - Clement Fung
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | - Kathleen E Houlahan
- Ontario Institute for Cancer Research, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Jianxin Wang
- Ontario Institute for Cancer Research, Toronto, Canada.,Present address: Center for Computational Research, Buffalo Institute for Genomics and Data Analytics, NYS Center for Excellence in Bioinformatics & Life Science, University at Buffalo, Buffalo, USA
| | | | | | - Ren X Sun
- Ontario Institute for Cancer Research, Toronto, Canada.,Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada
| | - Xihui Lin
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | - Aileen Lu
- Ontario Institute for Cancer Research, Toronto, Canada.,Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada
| | - Yu-Jia Shiah
- Ontario Institute for Cancer Research, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Amin Zia
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Ryan Kearns
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Paul C Boutros
- Ontario Institute for Cancer Research, Toronto, Canada. .,Department of Medical Biophysics, University of Toronto, Toronto, Canada. .,Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada. .,Department of Human Genetics, University of California, Los Angeles, USA. .,Department of Urology, University of California, Los Angeles, USA. .,Institute for Precision Health, University of California, Los Angeles, USA. .,Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA.
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