Opening book details…
Can I read BASIN: A Semi-automatic Workflow, with Machine Learning Segmentation, for Objective Statistical Analysis of Biomedical and Biofilm Image Datasets on EtoBox?
BASIN: A Semi-automatic Workflow, with Machine Learning Segmentation, for Objective Statistical Analysis of Biomedical and Biofilm Image Datasets by Timothy W. Hartman; Evgeni Radichev; Hafiz Munsub Ali; Mathew Olakunle Alaba; Mariah Hoffman; Gideon Kassa; Rajesh Sani; Venkata Gadhamshetty; Shankarachary Ragi; Shanta M. Messerli; Pilar de la Puente; Eric S. Sandhurst; Tuyen Do; Carol Lushbough; Etienne Z. Gnimpieba is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
What is BASIN: A Semi-automatic Workflow, with Machine Learning Segmentation, for Objective Statistical Analysis of Biomedical and Biofilm Image Datasets about?
Micrograph comparison remains useful in bioscience. This technology provides researchers with a quick snapshot of experimental conditions. But sometimes a two- condition comparison relies on researchers’ eyes to draw conclusions. Our Bioimage Analysis, Statistic, and Comparison (BASIN) software provides an objective and reproducible comparison leveraging inferential statistics to bridge image data with other modalities. Users have access to machine learning-based object segmentation. BASIN provides several data points such as images’ object counts, intensities, and areas. Hypothesis testing may also be performed. To improve BASIN’s accessibility, we implemented it using R Shiny and provided both an online and offline version. We used BASIN to process 498 image pairs involving five bioscience topics. Our framework supported either direct claims or extrapolations 57% of the time. Analysis results were manually curated to determine BASIN’s accuracy which was shown to be 78%. Additionally, each BASIN version’s initial release shows an average 82% FAIR compliance score.
Who reads BASIN: A Semi-automatic Workflow, with Machine Learning Segmentation, for Objective Statistical Analysis of Biomedical and Biofilm Image Datasets?
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Timothy W. Hartman; Evgeni Radichev; Hafiz Munsub Ali; Mathew Olakunle Alaba; Mariah Hoffman; Gideon Kassa; Rajesh Sani; Venkata Gadhamshetty; Shankarachary Ragi; Shanta M. Messerli; Pilar de la Puente; Eric S. Sandhurst; Tuyen Do; Carol Lushbough; Etienne Z. Gnimpieba
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)