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Systems biologists and cancer researchers require interactive visualization tools that enable them to more easily navigate and discover patterns at different levels of the biological hierarchy of signaling pathways. Furthermore, biologists are often interested in understanding and exploring the causal biochemical links between processes. When exploring the literature of particular biological pathways...
Annotation plays an important role in conveying key points in visual data-driven storytelling; it helps presenters explain and emphasize core messages and specific data. However, the visualization research community has a limited understanding of annotation and its role in data-driven storytelling, and existing charting software provides limited support for creating annotations. In this paper, we...
Biclustering is a well-known approach for data mining, and it is applied in many fields, such as genome analyses, security services, and social network analyses. Biclustering finds bicliques contained in a bipartite graph. However, in real data, a biclique may lack several edges because of various reasons, such as errors. In this situation, traditional biclustering methods cannot find correct biclusters...
Neuroscientists study brain functional connectivity in order to obtain a deeper understanding of how the brain functions. Current studies are mainly based on analyzing the averaged brain connectivity of a group (or groups) due to the high complexity of the collected data in terms of dimensionality, variability, and volume. While it is more desirable for the researchers to explore the potential variability...
Datasets obtained through recently advanced measurement techniques tend to possess a large number of dimensions. This leads to explosively increasing computation costs for analyzing such datasets, thus making formulation and verification of scientific hypotheses very difficult. Therefore, an efficient approach to identifying feature subspaces of target datasets, that is, the subspaces of dimension...
We introduce the concept of “spatio-data coordination” (SD coordination) which defines the mapping of user actions in physical space into the space of data in a visualisation. SD coordination is intended to lower the user's cognitive load when exploring complex multi-dimensional data such as biomedical data, multiple data attributes vs time in a space-time-cube visualisation, or three-dimensional...
Atmospheric sciences is the study of physical and chemical phenomena occurring within the Earth's atmosphere. The study entails understanding the state of the Earth's atmosphere, how it is changing over time and why. Understanding how various weather events develop and evolve is often conducted through retrospective analysis of past atmospheric events. Atmospheric scientists can then utilize tools...
High-resolution simulation data sets provide plethora of information, which needs to be explored by application scientists to gain enhanced understanding about various phenomena. Visual-analytics techniques using raw data sets are often expensive due to the data sets' extreme sizes. But, interactive analysis and visualization is crucial for big data analytics, because scientists can then focus on...
In this work, we present Interaction+, a tool that enhances the interactive capability of existing web-based visualizations. Different from the toolkits for authoring interactions during the visualization construction, Interaction+ takes existing visualizations as input, analyzes the visual objects, and provides users with a suite of interactions to facilitate the visual exploration, including selection,...
As virtual reality (VR) hardware technology becomes more mature and affordable, it is timely to develop visualization applications making use of such technology. How to interact with data in an immersive 3D space is both an interesting and challenging problem, demanding more research investigations. In this paper, we present a gesture input system for graph visualization in a stereoscopic 3D space...
Biological interpretation and understanding of machine learning based predictive models are highly desirable in healthcare analytics. Predicting Adverse Drug Reactions (ADRs) is extremely important for safe and precision medicine. There are various machine learning based approaches to predict adverse reactions for drugs. These models, though effective, lack biological interpretation and are treated...
Local distribution search is used in query-driven visualization for identifying salient features. Due to the high computational and storage costs, local distribution search in multi-field datasets is challenging. In this paper, we introduce two high performance, memory efficient algorithms for searching for local distributions that are characterized by marginal and joint features in multi-field datasets...
Analyzing social network data helps sociologists understand the behaviors of individuals and groups as well as the relationships between them. With additional ontology information, the semantics behind the network structure can be further explored. Unfortunately, creating network visualizations with these datasets for presentation can inadvertently expose the private and sensitive information of individuals...
The field of information visualization studies the interactive visual representations of data to reinforce human cognition, thereby facilitate the discovery of new tacit knowledge and even amplify human intelligence. Augmented reality (AR) shares the same objective and it can be treated as one particular form of information visualization where the data are both the real objects and the augmentations...
To solve the world's challenges requires not only advancing computer science and big data analytics but requires new analysis and decision-making environments that effectively couple human decision making with advanced, guided analytics in a human-computer collaborative discourse and decision making (HCCD). While many researchers and companies are focusing solely on Big Data Analytics to harness the...
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