Data Fusion in Systems Biology of a Social Amoeba Dictyostelium

In everyday life, we prefer to make decisions by considering all the available information, and often find that the inclusion of even seemingly circumstantial evidence provides an advantage. In the natural sciences, incorporating all the data can be difficult, time consuming and imprecise, if even possible. We have recently developed a method for large¬scale data fusion. Our fundamental question is: can we improve the results of predictive data mining by fusing numerous, sometimes loosely related, heterogeneous datasets? In this bilateral project, we will use computational approach of data fusion to simultaneously consider all the available genome¬scale data sets for a social amoeba Dictyostelium. Integration will include gene expression data sets, data on protein binding, mutant phenotype data, data on gene annotations and pathway assignments, pathway ontologies, data on KEGG pathways, data on and chemicals and their structural properties, and data from the literature.

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