Visualization of high throughput biological data

The rapid advances in high throughput biotechnology pose great challenges to the data analysis and visualization community. The sheer volume of data and complex biological problems that need to be answered increase the demand for effective data analysis and visualization tools which provide intuitive visual representation and allow full exploitation of the data. In this paper, we examine various visualization techniques that have been applied to high throughput biological data analysis. Several key problem areas as well as possible solutions are explored, and some challenging open issues are highlighted.

[1]  D. L. Taylor,et al.  High content screening applied to large-scale cell biology. , 2004, Trends in biotechnology.

[2]  Ian H. Witten,et al.  WEKA: a machine learning workbench , 1994, Proceedings of ANZIIS '94 - Australian New Zealnd Intelligent Information Systems Conference.

[3]  Anne E Carpenter,et al.  Visualization of image data from cells to organisms , 2010, Nature Methods.

[4]  M. Bittner,et al.  Data management and analysis for gene expression arrays , 1998, Nature Genetics.

[5]  Abhishek Tiwari,et al.  Workflow based framework for life science informatics , 2007, Comput. Biol. Chem..

[6]  Duncan Temple Lang,et al.  GGobi: evolving from XGobi into an extensible framework for interactive data visualization , 2003, Comput. Stat. Data Anal..

[7]  Ben Shneiderman,et al.  The Craft of Information Visualization: Readings and Reflections , 2003 .

[8]  Enrico Bertini,et al.  Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization , 2011, IEEE Transactions on Visualization and Computer Graphics.