Visual analysis of gene expression data by means of biclustering
Bioinformatics manages large amounts of data. An example of this is gene expression analysis: 10K to 100K genes or probes tested under several conditions.
Large expression matrices , complex analysis results and several sources of biological knowledge demand use of the full cognitive capabilites of the analyst, both abstract or visual.
My thesis approaches these issues by means of novel visualization techniques and the integration of data, analysis and visualization. The result will contribute to improve the reasoning process related to gene expression analysis by means of a visual analytics perspective.