FlyExpress: visual mining of spatiotemporal patterns for genes and publications in Drosophila embryogenesis.

SUMMARY: Images containing spatial expression patterns illuminate the roles of different genes during embryogenesis. In order to generate initial clues to regulatory interactions, biologists frequently need to know the set of genes expressed at the same time at specific locations in a developing embryo, as well as related research publications. However, text-based mining of image annotations and research articles cannot produce all relevant results, because the primary data are images that exist as graphical objects. We have developed a unique knowledge base (FlyExpress) to facilitate visual mining of images from Drosophila melanogaster embryogenesis. By clicking on specific locations in pictures of fly embryos from different stages of development and different visual projections, users can produce a list of genes and publications instantly. In FlyExpress, each queryable embryo picture is a heat-map that captures the expression patterns of more than 4500 genes and more than 2600 published articles. In addition, one can view spatial patterns for particular genes over time as well as find other genes with similar expression patterns at a given developmental stage. Therefore, FlyExpress is a unique tool for mining spatiotemporal expression patterns in a format readily accessible to the scientific community. AVAILABILITY: http://www.flyexpress.net CONTACT: s.kumar@asu.edu.

Identification of pneumonia and influenza deaths using the Death Certificate Pipeline.

BACKGROUND: Death records are a rich source of data, which can be used to assist with public surveillance and/or decision support. However, to use this type of data for such purposes it has to be transformed into a coded format to make it computable. Because the cause of death in the certificates is reported as free text, encoding the data is currently the single largest barrier of using death certificates for surveillance. Therefore, the purpose of this study was to demonstrate the feasibility of using a pipeline, composed of a detection rule and a natural language processor, for the real time encoding of death certificates using the identification of pneumonia and influenza cases as an example and demonstrating that its accuracy is comparable to existing methods. RESULTS: A Death Certificates Pipeline (DCP) was developed to automatically code death certificates and identify pneumonia and influenza cases. The pipeline used MetaMap to code death certificates from the Utah Department of Health for the year 2008. The output of MetaMap was then accessed by detection rules which flagged pneumonia and influenza cases based on the Centers of Disease and Control and Prevention (CDC) case definition. The output from the DCP was compared with the current method used by the CDC and with a keyword search. Recall, precision, positive predictive value and F-measure with respect to the CDC method were calculated for the two other methods considered here. The two different techniques compared here with the CDC method showed the following recall/ precision results: DCP: 0.998/0.98 and keyword searching: 0.96/0.96. The F-measure were 0.99 and 0.96 respectively (DCP and keyword searching). Both the keyword and the DCP can run in interactive form with modest computer resources, but DCP showed superior performance. CONCLUSION: The pipeline proposed here for coding death certificates and the detection of cases is feasible and can be extended to other conditions. This method provides an alternative that allows for coding free-text death certificates in real time that may increase its utilization not only in the public health domain but also for biomedical researchers and developers. TRIAL REGISTRATION: This study did not involved any clinical trials.