Molecular Visualization of Computational Biology Data: A Survey of Surveys

Alharbi, Naif Khalaf; Alharbi, Mohammad; Martinez, Xavier; Krone, Michael; Rose, Alexander; Baaden, Marc; Laramee, Robert S.; Chavent, Matthieu

doi:10.2312/eurovisshort.20171146

Cited by 4 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We investigate multi-scale representations of the DNA, which relates to work in bio-molecular visualization. Several surveys have summarized work in this field [2,28,29,39], so below we only point out selected approaches. In addition, a large body of work by professional illustrators on mesoscale cell depiction inspired us such as visualizing the human chromosome down to the detail of individual parts of the molecule [19].…”

Section: Scale-dependent Molecular and Genome Visualizationmentioning

confidence: 99%

Scale Trotter: Illustrative Visual Travels Across Negative Scales

Halladjian

Miao

Kouril

et al. 2020

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

NucleusChromosomes Chromosomes with detail Nucleosomes Nucleotides Atoms Fibers Fig. 1. Steps along the voyage into the genomic detail as enabled by ScaleTrotter, showing the semantic scale levels. ScaleTrotter and its visual embedding allow us to seamlessly transition between independent representations and interactively explore them.Abstract-We present ScaleTrotter, a conceptual framework for an interactive, multi-scale visualization of biological mesoscale data and, specifically, genome data. ScaleTrotter allows viewers to smoothly transition from the nucleus of a cell to the atomistic composition of the DNA, while bridging several orders of magnitude in scale. The challenges in creating an interactive visualization of genome data are fundamentally different in several ways from those in other domains like astronomy that require a multi-scale representation as well. First, genome data has intertwined scale levels-the DNA is an extremely long, connected molecule that manifests itself at all scale levels. Second, elements of the DNA do not disappear as one zooms out-instead the scale levels at which they are observed group these elements differently. Third, we have detailed information and thus geometry for the entire dataset and for all scale levels, posing a challenge for interactive visual exploration. Finally, the conceptual scale levels for genome data are close in scale space, requiring us to find ways to visually embed a smaller scale into a coarser one. We address these challenges by creating a new multi-scale visualization concept. We use a scale-dependent camera model that controls the visual embedding of the scales into their respective parents, the rendering of a subset of the scale hierarchy, and the location, size, and scope of the view. In traversing the scales, ScaleTrotter is roaming between 2D and 3D visual representations that are depicted in integrated visuals. We discuss, specifically, how this form of multi-scale visualization follows from the specific characteristics of the genome data and describe its implementation. Finally, we discuss the implications of our work to the general illustrative depiction of multi-scale data.

show abstract

Section: Scale-dependent Molecular and Genome Visualizationmentioning

confidence: 99%

Scale Trotter: Illustrative Visual Travels Across Negative Scales

Halladjian

Miao

Kouril

et al. 2020

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

show abstract

“…There is a body of work on 3D visualizations of particle data in MD, SPH, and also other fields [2,17]. Since 3D representations are not central in our research, we default to the simplest, useful, particle representation by shaded spheres at particle positions with user-adjustable sizes.…”

Section: D Viewmentioning

confidence: 99%

Scale-Space Splatting: Reforming Spacetime for Cross-Scale Exploration of Integral Measures in Molecular Dynamics

Pálenik

Brückner

Hauser

2019

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Fig. 1: Novel representation of electrostatic interaction energy (in the middle) helps navigating the simulation by spatially resolving contributions to the integral measure traditionally represented by a timeseries (blue line in the bottom). It captures different modes of the simulation better than the timeseries and helps localizing changes in the configuration of the simulation by providing spatial context. The multiscale behaviour of the data is explored by seamlessly navigating the spatiotemporal scale-space. The selected timestep shows the ligand escaping from the tunnel, before it is sucked back in by the protein.Abstract-Understanding large amounts of spatiotemporal data from particle-based simulations, such as molecular dynamics, often relies on the computation and analysis of aggregate measures. These, however, by virtue of aggregation, hide structural information about the space/time localization of the studied phenomena. This leads to degenerate cases where the measures fail to capture distinct behaviour. In order to drill into these aggregate values, we propose a multi-scale visual exploration technique. Our novel representation, based on partial domain aggregation, enables the construction of a continuous scale-space for discrete datasets and the simultaneous exploration of scales in both space and time. We link these two scale-spaces in a scale-space space-time cube and model linked views as orthogonal slices through this cube, thus enabling the rapid identification of spatio-temporal patterns at multiple scales. To demonstrate the effectiveness of our approach, we showcase an advanced exploration of a protein-ligand simulation.

show abstract

“…The completion time it took the users to respond to each question and their answers to the multiple-choice questions are shown in Figure 8. 10. After the initial setting shown in Figure 8.4(a), all participants decided to exclude Generosity in Q1 (Answer: d, Q1), which happened in 2.03 minutes on average.…”

Section: Question-related Resultsmentioning

confidence: 99%

“…For this section, we validate our results with cross-validation using eight-folds, and we scan the hyperparameter space for 25 iterations, choosing the model with the best accuracy. The hyperparameters we experimented with (and their intervals) are: number of trees , learning rate (0.0-0.3), maximum depth of a tree (6)(7)(8)(9)(10)(11)(12), subsample ratio of the training instances (0.8-1.0), and fraction of columns to be subsampled (0.8-1.0). All those values can be easily adjusted within the code.…”

Section: System Overview and Use Casementioning

confidence: 99%

“…We also use cross-validation with three-folds on the training set, and we scan the hyperparameter space for 10 iterations using Random search [44] in each algorithm separately. The common hyperparameters for both ML algorithms we experimented with (and their intervals) are: number of trees/estimators (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), maximum depth of a tree (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25), and minimum samples in each leaf of a tree (1-10). An extra hyperparameter of RF is the maximum number of features to consider when looking for the best split (( _ _ )-( _ _ − 1)).…”

Section: System Overview and Use Casementioning

confidence: 99%

See 1 more Smart Citation

Visual Analytics for Explainable and Trustworthy Machine Learning

Chatzimparmpas¹

View full text Add to dashboard Cite

The deployment of artificial intelligence solutions and machine learning research has exploded in popularity in recent years, with numerous types of models proposed to interpret and predict patterns and trends in data from diverse disciplines. However, as the complexity of these models grows, it becomes increasingly difficult for users to evaluate and rely on the model results, since their inner workings are mostly hidden in black boxes, which are difficult to trust in critical decision-making scenarios. While automated methods can partly handle these problems, recent research findings suggest that their combination with innovative methods developed within information visualization and visual analytics can lead to further insights gained from models and, consequently, improve their predictive ability and enhance trustworthiness in the entire process. Visual analytics is the area of research that studies the analysis of vast and intricate information spaces by combining statistical and machine learning models with interactive visual interfaces. By following this methodology, human experts can better understand such spaces and apply their domain expertise in the process of building and improving the underlying models. The primary goals of this dissertation are twofold, focusing on (1) methodological aspects, by conducting qualitative and quantitative meta-analyses to support the visualization research community in making sense of its literature and to highlight unsolved challenges, as well as (2) technical solutions, by developing visual analytics approaches for various machine learning models, such as dimensionality reduction and ensemble learning methods. Regarding the first goal, we define, categorize, and examine in depth the means for visual coverage of the different trust levels at each stage of a typical machine learning pipeline and establish a design space for novel visualizations in the area. Regarding the second goal, we discuss multiple visual analytics tools and systems implemented by us to facilitate the underlying research on the various stages of the machine learning pipeline, i.e., data processing, feature engineering, hyperparameter tuning, understanding, debugging, refining, and comparing models. Our approaches are data-agnostic, but mainly target tabular data with meaningful attributes in diverse domains, such as health care and finance. The applicability and effectiveness of this work were validated with case studies, usage scenarios, expert interviews, user studies, and critical discussions of limitations and alternative designs. The results of this dissertation provide new avenues for visual analytics research in explainable and trustworthy machine learning. Keywords: visualization, interaction, visual analytics, explainable machine learning, XAI, trustworthy machine learning, ensemble learning, dimensionality reduction, supervised learning, unsupervised learning, ML, AI, tabular data

show abstract

Molecular Visualization of Computational Biology Data: A Survey of Surveys

Cited by 4 publications

References 0 publications

Scale Trotter: Illustrative Visual Travels Across Negative Scales

Scale Trotter: Illustrative Visual Travels Across Negative Scales

Scale-Space Splatting: Reforming Spacetime for Cross-Scale Exploration of Integral Measures in Molecular Dynamics

Visual Analytics for Explainable and Trustworthy Machine Learning

Contact Info

Product

Resources

About