Signalling maps in cancer research: construction and data analysis

Generation and usage of high-quality molecular signalling network maps can be augmented by standardising notations, establishing curation workflows and application of computational biology methods to exploit the knowledge contained in the maps. In this manuscript, we summarize the major aims and challenges of assembling information in the form of comprehensive maps of molecular interactions. Mainly, we share our experience gained while creating the Atlas of Cancer Signalling Network. In the step-by-step procedure, we describe the map construction process and suggest solutions for map complexity management by introducing a hierarchical modular map structure. In addition, we describe the NaviCell platform, a computational technology using Google Maps API to explore comprehensive molecular maps similar to geographical maps, and explain the advantages of semantic zooming principles for map navigation. We also provide the outline to prepare signalling network maps for navigation using the NaviCell platform. Finally, several examples of cancer high-throughput data analysis and visualization in the context of comprehensive signalling maps are presented.

Multi-aspect visual analytics on large-scale high-dimensional cyber security data

In this article, we present a visual analytics system, SemanticPrism, which aims to analyze large-scale high-dimensional cyber security datasets containing logs of a million computers. SemanticPrism visualizes the data from three different perspectives: spatiotemporal distribution, overall temporal trends, and pixel-based IP (Internet Protocol) address blocks. With each perspective, we use semantic zooming to present more detailed information. The interlinked visualizations and multiple levels of detail allow us to detect unexpected changes taking place in different dimensions of the data and to identify potential anomalies in the network. After comparing our approach to other submissions, we outline potential paths for future improvement.

Evaluation of Semantic Fisheye Zooming to Provide Focus+Context

A concept map is a diagram that consists of nodes that contain individual concepts or pieces of information. These nodes are connected by lines that represent relationships between the information. Large concept maps are difficult to explore and navigate using current digital display interfaces. As users zoom in on a desired node, connections between the node of interest and surrounding nodes become hidden from the user. A combination of fisheye zooming and semantic zooming mechanisms to maintain the visual connections between the nodes was implemented, and a user study to determine whether this technique helps users learn from the map was conducted. The user study revealed that participants were able to recall more information presented in a concept map, with practically no difference in the amount of time spent using the map, despite the novelty of the semantic fisheye interface.