sMolBoxes: Dataflow Model for Molecular Dynamics Exploration

We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.acceptedVersio

sMolBoxes: Dataflow Model for Molecular Dynamics Exploration

We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case study illustrates that even with relatively few sMolBoxes, it is possible to express complex analyses tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.Comment: 10 pages, 9 figures, IEEE VIS, TVC

ANALYZA – Vizualizační komponenta – Visilant

Softwarová vizualizační komponenta je nedílnou součástí systému ANALYZA, výrazně ovlivňující jak jeho analytické schopnosti, tak uživatelský komfort. Cílem této softwarové komponenty je poskytnout jednotný systém podporující policejní vyšetřování, který umožní policejním silám přístup, analýzu, vizualizaci a společné sdílení příslušných údajů nebo informací. Frontend systému je složen z komplexních vizualizací na pozadí využívajících různé technologie datové analýzy (integrované analytické operace), které umožňují průzkum, efektivní rozhodování a uvažování o datech způsobem, který je intuitivní a srozumitelný datovým analytikům.The visualization software component is an integral part of the ANALYZA system, significantly influencing both its analytical capabilities and user comfort. The component's goal is to provide a unified system supporting criminal investigation, which will allow police investigators to access, analyze, visualize and share relevant data or information. The system's frontend consists of complex visualizations using various data analysis technologies (integrated analytical operations), which enable research, effective decision-making, and thinking about data intuitively and understandably to data analysts

Seeing the unseen: Comparison study of representation approaches for biochemical processes in education.

The representations of biochemical processes must balance visual portrayals with descriptive content to be an effective learning tool. To determine what type of representation is the most suitable for education, we designed five different representations of adenosine triphosphate (ATP) synthesis and examined how they are perceived. Our representations consisted of an overview of the process in a detailed and abstract illustrative format, continuous video formats with and without narration, and a combined illustrative overview with dynamic components. The five representations were evaluated by non-experts who were randomly assigned one of them and experts who viewed and compared all five representations. Subsequently, we conducted a focus group on the outcomes of these evaluations, which gave insight into possible explanations of our results, where the non-experts preferred the detailed static representation and found the narrated video least helpful, in contradiction to the experts who favored the narrated video the most

Multiscale Visual Drilldown for the Analysis of Large Ensembles of Multi-Body Protein Complexes

When studying multi-body protein complexes, biochemists use computational tools that can suggest hundreds or thousands of their possible spatial configurations. However, it is not feasible to experimentally verify more than only a very small subset of them. In this paper, we propose a novel multiscale visual drilldown approach that was designed in tight collaboration with proteomic experts, enabling a systematic exploration of the configuration space. Our approach takes advantage of the hierarchical structure of the data - from the whole ensemble of protein complex configurations to the individual configurations, their contact interfaces, and the interacting amino acids. Our new solution is based on interactively linked 2D and 3D views for individual hierarchy levels. At each level, we offer a set of selection and filtering operations that enable the user to narrow down the number of configurations that need to be manually scrutinized. Furthermore, we offer a dedicated filter interface, which provides the users with an overview of the applied filtering operations and enables them to examine their impact on the explored ensemble. This way, we maintain the history of the exploration process and thus enable the user to return to an earlier point of the exploration. We demonstrate the effectiveness of our approach on two case studies conducted by collaborating proteomic experts

Additional file 3 of COZOID: contact zone identifier for visual analysis of protein-protein interactions

Example data. Testing dataset used in the manuscript. (ZIP 2662 kb

Additional file 2 of COZOID: contact zone identifier for visual analysis of protein-protein interactions

Software build. Executable binary file of the software tool. (ZIP 118,784 kb

PREVIS: Predictive visual analytics of anatomical variability for radiotherapy decision support

Radiotherapy (RT) requires meticulous planning prior to treatment, where the RT plan is optimized with organ delineations on a pre-treatment Computed Tomography (CT) scan of the patient. The conventionally fractionated treatment usually lasts several weeks. Random changes (e.g., rectal and bladder filling in prostate cancer patients) and systematic changes (e.g., weight loss) occur while the patient is being treated. Therefore, the delivered dose distribution may deviate from the planned. Modern technology, in particular image guidance, allows to minimize these deviations, but risks for the patient remain. We present PREVIS: a visual analytics tool for (i) the exploration and prediction of changes in patient anatomy during the upcoming treatment, and (ii) the assessment of treatment strategies, with respect to the anticipated changes. Records of during-treatment changes from a retrospective imaging cohort with complete data are employed in PREVIS, to infer expected anatomical changes of new incoming patients with incomplete data, using a generative model. Abstracted representations of the retrospective cohort partitioning provide insight into an underlying automated clustering, showing main modes of variation for past patients. Interactive similarity representations support an informed selection of matching between new incoming patients and past patients. A Principal Component Analysis (PCA)-based generative model describes the predicted spatial probability distributions of the incoming patient's organs in the upcoming weeks of treatment, based on observations of past patients. The generative model is interactively linked to treatment plan evaluation, supporting the selection of the optimal treatment strategy. We present a usage scenario, demonstrating the applicability of PREVIS in a clinical research setting, and we evaluate our visual analytics tool with eight clinical researchers

Additional file 4 of COZOID: contact zone identifier for visual analysis of protein-protein interactions

User guide. User guide for the software tool. (PDF 3502 kb