Bob Baddeley

Learn More
A central challenge in visual analytics is the creation of accessible, widely distributable analysis applications that bring the benefits of visual discovery to as broad a user base as possible. Moreover, to support the role of visualization in the knowledge creation process, it is advantageous to allow users to describe the reasoning strategies they employ(More)
We present a visualization environment called the Scalable Reasoning System (SRS) that provides a suite of tools for the collection, analysis, and dissemination of reasoning products. This environment is designed to function across multiple platforms, bringing the display of visual information and the capture of reasoning associated with that information to(More)
We present the design and implementation of InfoStar, an adaptive visual analytics platform for mobile devices such as PDAs, laptops, Tablet PCs and mobile phones. InfoStar extends the reach of visual analytics technology beyond the traditional desktop paradigm to provide ubiquitous access to interactive visualizations of information spaces. These(More)
The ability to support creation and parallel analysis of al. 2004, Good et al. 2004, Stech 2004, Pope et al. 2005) competing scenarios is perhaps the greatest single challenge for typically relate hypotheses directly to evidence. While today's intelligence analysis systems. Dempster-Shafer theory adequate for relatively small and simple scenarios, this(More)
Most current approaches to automatic pathway generation are based on a reverse engineering approach in which pathway plausibility is solely derived from gene expression data and not independently validated. Alternative approaches use prior biological knowledge to validate automatically inferred pathways, but the prior knowledge is usually not sufficiently(More)
Increasingly, reverse engineering methods have been employed to infer transcriptional regulatory networks from gene expression data. Enrichment with independent evidence from sources such as the biomedical literature and the Gene Ontology (GO) is desirable to corroborate, annotate and expand these networks as well as manually constructed networks. In this(More)
A variety of methods and algorithms have recently been employed in the analysis of gene expression data, including reverse-engineering and knowledge-based pathway modeling, semantic gene similarity, network analysis and clustering. These methods and algorithms address different subparts of the same overall challenge and need to be applied in combination to(More)
Microarray gene expression data provide a unique information resource for learning biological networks using "reverse engineering" methods. However, there are a variety of cases in which we know which genes are involved in a given pathology of interest, but we do not have enough experimental evidence to support the use of(More)