Evaluation of two interaction techniques for visualization of dynamic graphs

Several techniques for visualization of dynamic graphs are based on different spatial arrangements of a temporal sequence of node-link diagrams. Many studies in the literature have investigated the importance of maintaining the user's mental map across this temporal sequence, but usually each layout is considered as a static graph drawing and the effect of user interaction is disregarded. We conducted a task-based controlled experiment to assess the effectiveness of two basic interaction techniques: the adjustment of the layout stability and the highlighting of adjacent nodes and edges. We found that generally both interaction techniques increase accuracy, sometimes at the cost of longer completion times, and that the highlighting outclasses the stability adjustment for many tasks except the most complex ones.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016

MONGKIE: an integrated tool for network analysis and visualization for multi-omics data

BACKGROUND: Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. RESULTS: Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. CONCLUSION: We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. REVIEWERS: This article was reviewed by Prof. Limsoon Wong, Prof. Soojin Yi, and Prof. David Kreil. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13062-016-0112-y) contains supplementary material, which is available to authorized users

TripVista: Triple Perspective Visual Trajectory Analytics and its application on microscopic traffic data at a road intersection

In this paper, we present an interactive visual analytics system, Triple Perspective Visual Trajectory Analytics (TripVista), for exploring and analyzing complex traffic trajectory data. The users are equipped with a carefully designed interface to inspect data interactively from three perspectives (spatial, temporal and multidimensional views). While most previous works, in both visualization and transportation research, focused on the macro aspects of traffic flows, we develop visualization methods to investigate and analyze microscopic traffic patterns and abnormal behaviors. In the spatial view of our system, traffic trajectories with various presentation styles are directly interactive with user brushing, together with convenient pattern exploration and selection through ring-style sliders. Improved ThemeRiver, embedded with glyphs indicating directional information, and multiple scatterplots with time as horizontal axes illustrate temporal information of the traffic flows. Our system also harnesses the power of parallel coordinates to visualize the multi-dimensional aspects of the traffic trajectory data. The above three view components are linked closely and interactively to provide access to multiple perspectives for users. Experiments show that our system is capable of effectively finding both regular and abnormal traffic flow patterns.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000316816300021&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Theory & MethodsEngineering, Electrical & ElectronicEICPCI-S(ISTP)2

Integrative computational biology for cancer research

Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed clinical cancer research. They have revealed novel molecular markers of cancer subtypes, metastasis, and drug sensitivity and resistance. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and great volumes of data are accumulating at a rapid pace. Here we discuss these challenges, and show how integrative computational biology can help diminish them by creating new software tools, analytical methods, and data standards

Interactive Visualization Lenses:: Natural Magic Lens Interaction for Graph Visualization

Information visualization is an important research field concerned with making sense and inferring knowledge from data collections. Graph visualizations are specific techniques for data representation relevant in diverse application domains among them biology, software-engineering, and business finance. These data visualizations benefit from the display space provided by novel interactive large display environments. However, these environments also cause new challenges and result in new requirements regarding the need for interaction beyond the desktop and according redesign of analysis tools. This thesis focuses on interactive magic lenses, specialized locally applied tools that temporarily manipulate the visualization. These may include magnification of focus regions but also more graph-specific functions such as pulling in neighboring nodes or locally reducing edge clutter. Up to now, these lenses have mostly been used as single-user, single-purpose tools operated by mouse and keyboard. This dissertation presents the extension of magic lenses both in terms of function as well as interaction for large vertical displays. In particular, this thesis contributes several natural interaction designs with magic lenses for the exploration of graph data in node-link visualizations using diverse interaction modalities. This development incorporates flexible switches between lens functions, adjustment of individual lens properties and function parameters, as well as the combination of lenses. It proposes interaction techniques for fluent multi-touch manipulation of lenses, controlling lenses using mobile devices in front of large displays, and a novel concept of body-controlled magic lenses. Functional extensions in addition to these interaction techniques convert the lenses to user-configurable, personal territories with use of alternative interaction styles. To create the foundation for this extension, the dissertation incorporates a comprehensive design space of magic lenses, their function, parameters, and interactions. Additionally, it provides a discussion on increased embodiment in tool and controller design, contributing insights into user position and movement in front of large vertical displays as a result of empirical investigations and evaluations.Informationsvisualisierung ist ein wichtiges Forschungsfeld, das das Analysieren von Daten unterstützt. Graph-Visualisierungen sind dabei eine spezielle Variante der Datenrepräsentation, deren Nutzen in vielerlei Anwendungsfällen zum Einsatz kommt, u.a. in der Biologie, Softwareentwicklung und Finanzwirtschaft. Diese Datendarstellungen profitieren besonders von großen Displays in neuen Displayumgebungen. Jedoch bringen diese Umgebungen auch neue Herausforderungen mit sich und stellen Anforderungen an Nutzerschnittstellen jenseits der traditionellen Ansätze, die dadurch auch Anpassungen von Analysewerkzeugen erfordern. Diese Dissertation befasst sich mit interaktiven „Magischen Linsen“, spezielle lokal-angewandte Werkzeuge, die temporär die Visualisierung zur Analyse manipulieren. Dabei existieren zum Beispiel Vergrößerungslinsen, aber auch Graph-spezifische Manipulationen, wie das Anziehen von Nachbarknoten oder das Reduzieren von Kantenüberlappungen im lokalen Bereich. Bisher wurden diese Linsen vor allem als Werkzeug für einzelne Nutzer mit sehr spezialisiertem Effekt eingesetzt und per Maus und Tastatur bedient. Die vorliegende Doktorarbeit präsentiert die Erweiterung dieser magischen Linsen, sowohl in Bezug auf die Funktionalität als auch für die Interaktion an großen, vertikalen Displays. Insbesondere trägt diese Dissertation dazu bei, die Exploration von Graphen mit magischen Linsen durch natürliche Interaktion mit unterschiedlichen Modalitäten zu unterstützen. Dabei werden flexible Änderungen der Linsenfunktion, Anpassungen von individuellen Linseneigenschaften und Funktionsparametern, sowie die Kombination unterschiedlicher Linsen ermöglicht. Es werden Interaktionstechniken für die natürliche Manipulation der Linsen durch Multitouch-Interaktion, sowie das Kontrollieren von Linsen durch Mobilgeräte vor einer Displaywand vorgestellt. Außerdem wurde ein neuartiges Konzept körpergesteuerter magischer Linsen entwickelt. Funktionale Erweiterungen in Kombination mit diesen Interaktionskonzepten machen die Linse zu einem vom Nutzer einstellbaren, persönlichen Arbeitsbereich, der zudem alternative Interaktionsstile erlaubt. Als Grundlage für diese Erweiterungen stellt die Dissertation eine umfangreiche analytische Kategorisierung bisheriger Forschungsarbeiten zu magischen Linsen vor, in der Funktionen, Parameter und Interaktion mit Linsen eingeordnet werden. Zusätzlich macht die Arbeit Vor- und Nachteile körpernaher Interaktion für Werkzeuge bzw. ihre Steuerung zum Thema und diskutiert dabei Nutzerposition und -bewegung an großen Displaywänden belegt durch empirische Nutzerstudien

Computational Approaches To Anti-Toxin Therapies And Biomarker Identification

This work describes the fundamental study of two bacterial toxins with computational methods, the rational design of a potent inhibitor using molecular dynamics, as well as the development of two bioinformatic methods for mining genomic data. Clostridium difficile is an opportunistic bacillus which produces two large glucosylating toxins. These toxins, TcdA and TcdB cause severe intestinal damage. As Clostridium difficile harbors considerable antibiotic resistance, one treatment strategy is to prevent the tissue damage that the toxins cause. The catalytic glucosyltransferase domain of TcdA and TcdB was studied using molecular dynamics in the presence of both a protein-protein binding partner and several substrates. These experiments were combined with lead optimization techniques to create a potent irreversible inhibitor which protects 95% of cells in vitro. Dynamics studies on a TcdB cysteine protease domain were performed to an allosteric communication pathway. Comparative analysis of the static and dynamic properties of the TcdA and TcdB glucosyltransferase domains were carried out to determine the basis for the differential lethality of these toxins. Large scale biological data is readily available in the post-genomic era, but it can be difficult to effectively use that data. Two bioinformatics methods were developed to process whole-genome data. Software was developed to return all genes containing a motif in single genome. This provides a list of genes which may be within the same regulatory network or targeted by a specific DNA binding factor. A second bioinformatic method was created to link the data from genome-wide association studies (GWAS) to specific genes. GWAS studies are frequently subjected to statistical analysis, but mutations are rarely investigated structurally. HyDn-SNP-S allows a researcher to find mutations in a gene that correlate to a GWAS studied phenotype. Across human DNA polymerases, this resulted in strongly predictive haplotypes for breast and prostate cancer. Molecular dynamics applied to DNA Polymerase Lambda suggested a structural explanation for the decrease in polymerase fidelity with that mutant. When applied to Histone Deacetylases, mutations were found that alter substrate binding, and post-translational modification

3D Generalization of brain model to visualize and analyze neuroanatomical data

Neuroscientists present data in a 3D form in order to convey a better real world visualization and understanding of the localization of data in relation to brain anatomy and structure. The problem with the visualization of cortical surface of the brain is that the brain has multiple, deep folds and the resulting structural overlap can hide data interweaved within the folds. On one hand, a 2D representation can result in a distorted view that may lead to incorrect localization and analysis of the data. On the other hand, a realistic 3D representation may interfere with our judgment or analysis by showing too many details. Alternatively, a 3D generalization can be used to simplify the model of the brain in order to visualize the hidden data and smooth some of the details. This dissertation addresses the following research question: Is 3D generalization of a brain model a viable approach for visualizing neuroanatomical data

Visualisation interactive de graphes (élaboration et optimisation d'algortihmes à coûts computationnels élevés)

Un graphe est un objet mathématique modélisant des relations sur un ensemble d'éléments. Il est utilisé dans de nombreux domaines à des fins de modélisation. La taille et la complexité des graphes manipulés de nos jours entraînentdes besoins de visualisation afin de mieux les analyser. Dans cette thèse, nous présentons différents travaux en visualisation interactive de graphes qui s'attachent à exploiter les architectures de calcul parallèle (CPU et GPU) disponibles sur les stations de travail contemporaines. Un premier ensemble de travaux s'intéresse à des problématiques de dessin de graphes. Dessiner un graphe consiste à le plonger visuellement dans un plan ou un espace. La première contribution dans cette thématique est un algorithmede regroupement d'arêtes en faisceaux appelé Winding Roads.Cet algorithme intuitif, facilement implémentable et parallélisable permet de réduireconsidérablement les problèmes d'occlusion dans un dessin de graphedus aux nombreux croisements d'arêtes.La seconde contribution est une méthode permettant dedessiner un réseau métabolique complet. Ce type deréseau modélise l'ensemble des réactions biochimiquesse produisant dans les cellules d'un organise vivant.L'avantage de la méthode est de prendre en compte la décompositiondu réseau en sous-ensembles fonctionnels ainsi que de respecterles conventions de dessin biologique.Un second ensemble de travaux porte sur des techniques d'infographiepour la visualisation interactive de graphes. La première contribution dans cette thématique est une technique de rendude courbes paramétriques exploitant pleinement le processeur graphique. La seconde contribution est une méthodede rendu nommée Edge splatting permettant de visualiserla densité des faisceaux d'arêtes dans un dessin de grapheavec regroupement d'arêtes. La dernière contribution portesur des techniques permettant de mettre en évidence des sous-graphesd'intérêt dans le contexte global d'une visualisation de graphes.A graph is a mathematical object used to model relations over a set of elements.It is used in numerous fields for modeling purposes. The size and complexityof graphs manipulated today call a need for visualization to better analyze them.In that thesis, we introducedifferent works in interactive graph visualisation which aim at exploiting parallel computing architectures (CPU and GPU) available on contemporary workstations.A first set of works focuses on graph drawing problems.Drawing a graph consists of embedding him in a plane or a space.The first contribution in that theme is an edge bundling algorithmnamed Winding Roads. That intuitive, easyly implementable and parallelizable algorithmallows to considerably reduce clutter due to numerous edge crossings in a graph drawing.The second contribution is a method to draw a complete metabolicnetwork. That kind of network models the whole set of biochemical reactionsoccurring within cells of a living organism. The advantage of the methodis to take into account the decomposition of the network into functionnal subsetsbut also to respect biological drawing conventions.A second set of works focuses on computer graphics techniquesfor interactive graph visualisation. The first contributionin that theme is a technique for rendering parametric curvesthat fully exploits the graphical processor unit. The second contributionis a rendering technique named Edge splatting that allowsto visualize the bundles densities in an edge bundled layout. Thelast contribution introduces some techniques for emphasizingsub-graphs of interest in the global context of a graph visualization.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF