BioViz 2013 - International Symposium Bioinformatics Visualization (BioViz)

Visualization is an important, and often necessary, part of Bioinformatics. Sequence alignment, gene expression data, NMR spectra, protein networks, for example, all rely heavily on interpreting the data visually--both for discovery and validation. Bioinformatics data are typically complex, and data sizes routinely run into the gigabyte and terabyte ranges. Thus, even traditionally tractable polynomial running times for algorithms are not practicable. Visualization in these cases is used to select subsets of data that can be more readily processed and analyzed. Bioinformatics tools are used by a wide array of scientists most of whom are not typically computer scientists. Therefore, interacting with not only the visualization components, but also using the tools on the whole must be made efficient and reasonably intuitive. Human Computer Interaction (HCI), the study of how to improve this digital/human interface is becoming a very important aspect of bioinformatics as well.
Visualization in bioinformatics offers many significant challenges, and success is likely to come from a joining of many disparate disciplines and areas including computer graphics, high performance computing, cognitive science, HCI, computer science, data mining, molecular biology, and so on.
We invite papers on these important challenges in visualization in Bioinformatics. Papers will be refereed, appear in the conference proceedings, and be published by Conference Publishing Services (CPS : There is a tentative plan to select 5-7 high-quality papers that will be recommended for publication in a special visualization issue of well-known bioinformatics journal. TOPICS OF INTEREST:
§ Systems biology tools
§ Visualization of curation and annotation of genome data
§ Genome comparison visualization
§ Combining visual and computational methods of genome data exploration
§ High performance computing
§ Optimizations of existing visualizations e.g., dot plots
§ Visual querying of genome data
§ Collaborative visualization and mining for bioinformatics
§ Evaluation of visual data mining methods
§ Extensions of existing open tools e.g., cytoscape
§ HCI including case studies of bioinformatics visualization approaches
§ Cognitive approaches and explanations for visual data mining for bioinformatics
§ Surveys of existing tools