Integration and Visualization of Time Series Expression Data of Gene Regulatory Networks.

Time series expression experiments are used to measure the expression of thousands of genes at a time under certain conditions, such as disease or drug treatment. By evaluating the large amounts of data, scientists gather valuable knowledge on various biological questions. An important problem addressed by the study of time series experiments is the discovery of gene function, since it is still unknown for a large set of genes. A web application- Expression Data Visualiser (EDVis), that enables the integration, visualization and evaluation of time series expression data, was developed and evaluated in the course of the thesis. EDVis provides several methods for comparison of time courses: Euclidean distance, Pearson and Spearman correlation and Dynamic Time Warping algorithm. Thus, one can identify highly correlated curves which in turn determine a possible similar function. Furthermore, the tool can be used to construct user-defined regulatory networks which are essential for the study of celullar processes