A framework for uncertainty-aware visual analytics of proteins

Maack, Robin G. C.; Raymer, Michael L.; Wischgoll, Thomas; Hagen, Hans; Gillmann, Christina

doi:10.1016/j.cag.2021.05.011

Cited by 10 publications

(3 citation statements)

References 36 publications

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“…Possibility volume representation treats atomic positions as a three-dimensional grid in continuous space and colors the grid based on probability density functions. Maack et al [75] used volume rendering and combined techniques such as transparency and B-factor coloring to visualize the uncertainty in atomic positions, providing a visual analytical framework for protein data with uncertainty. This uncertainty captures variations in atomic positions due to imprecise measurements or multi-model calculations.…”

Section: Positional Uncertainty Visualization Technologymentioning

confidence: 99%

A Concise Review of Biomolecule Visualization

Li,

Wei

2024

CIMB

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The structural characteristics of biomolecules are a major focus in the field of structural biology. Molecular visualization plays a crucial role in displaying structural information in an intuitive manner, aiding in the understanding of molecular properties. This paper provides a comprehensive overview of core concepts, key techniques, and tools in molecular visualization. Additionally, it presents the latest research findings to uncover emerging trends and highlights the challenges and potential directions for the development of the field.

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Section: Positional Uncertainty Visualization Technologymentioning

confidence: 99%

A Concise Review of Biomolecule Visualization

Li,

Wei

2024

CIMB

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“…The UA protein analysis tool by Maack et al [37] is using the positional uncertainty of atoms, provided by an anisotropic or isotropic model of their movement, to highlight regions of high disorder. The tool visualizes the uncertainty in a 3D visualization and a 2D Ramachandran plot, supplying the user with additional information in a detailed statistics view.…”

Section: Analysis Of Proteinsmentioning

confidence: 99%

A Workflow to Systematically Design Uncertainty-Aware Visual Analytics Applications

Maack,

Raith,

Perez

et al. 2023

Preprint

Self Cite

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Visual Analytics (VA) is a paradigm for insight generation by using visual analysis techniques and automated reasoning by transforming data into hypotheses and visualization to extract new insights, feeding them back into the data. This enhances the data until the desired insight is found. Many applications use this principle to provide meaningful mechanisms to assist decision-makers in achieving their goals. This process can be affected by a variety of uncertainties that can interfere with the user decision-making process. Unfortunately, there is no methodical description and handling tool to systematically include uncertainty in VA. We provide a unified workflow to transform the classic VA cycle into an uncertainty-aware Visual Analytics (UAVA) cycle consisting of five steps. Three real-world applications represent examples of the implementation of the UAVA cycle and the described workflow to prove its usability.

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“…Alternative visualization methods for the uncertainty in secondary structure assignments are discussed by Hamada (2014) . Another example of uncertainty visualization for proteins is by Maack et al (2021) , who address the visual representation of uncertainty in the conformation of proteins.…”

Section: Visual Mappingmentioning

confidence: 99%

Uncertainty Visualization: Concepts, Methods, and Applications in Biological Data Visualization

Weiskopf

2022

Front. Bioinform.

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This paper provides an overview of uncertainty visualization in general, along with specific examples of applications in bioinformatics. Starting from a processing and interaction pipeline of visualization, components are discussed that are relevant for handling and visualizing uncertainty introduced with the original data and at later stages in the pipeline, which shows the importance of making the stages of the pipeline aware of uncertainty and allowing them to propagate uncertainty. We detail concepts and methods for visual mappings of uncertainty, distinguishing between explicit and implict representations of distributions, different ways to show summary statistics, and combined or hybrid visualizations. The basic concepts are illustrated for several examples of graph visualization under uncertainty. Finally, this review paper discusses implications for the visualization of biological data and future research directions.

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A framework for uncertainty-aware visual analytics of proteins

Cited by 10 publications

References 36 publications

A Concise Review of Biomolecule Visualization

A Concise Review of Biomolecule Visualization

A Workflow to Systematically Design Uncertainty-Aware Visual Analytics Applications

Uncertainty Visualization: Concepts, Methods, and Applications in Biological Data Visualization

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