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This talk combines two stories about the analysis of data associated with diseases. In the first, we introduce community detection in networks and use network representations of genetic virulence factor similarities between different uropathogenic E. coli strains to identify communities of these strains that are more similar to each other than to the rest of the studied population. We then discuss the clinical differences between these E. coli communities. In the second story, we investigate metabolomic data obtained from stool samples of hospitalized patients. We employ a variety of methods for handling this sparse data to generate a new classifier for the presence of C.difficile in the samples. Working closely with our clinical collaborators, we then obtain a wholly new and surprisingly simple and accurate measurement for detecting the presence of active C. difficile infections.