Getting poster data...
Nicolas F. Fernandez, Matthew R. Jones, Greg W. Gundersen, Qiaonan Duan, Andrew D. Rouillard, Joel T. Dudley, and Avi Ma’ayan (Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA )Gene expression signatures collected from genome-wide microarray experiments, where disease tissue is compared with normal tissue, provide a view of the molecular mechanisms of disease. However, inferring upstream regulators and potential drugs from these signatures remains difficult. Nosology X2K visualizes a multi-faceted approach to predict upstream transcription factor and kinase activity, and potential therapeutic drugs/compounds, for over 150 human disease signatures. These disease signatures are analyzed with the tool Expression2Kinases (X2K) to predict kinase activity from gene expression. Drugs/compounds that inhibit the activity of the predicted kinases, determined by KinomeSCAN, are also displayed within interactive multi-node-type subnetworks. Predictions of kinase and drug associations with disease signatures using signatures obtained from GEO and the LINCS-L1000 Connectivity Map are visualized as dynamic bar graphs; in addition users can compare inferred signaling pathways for many diseases through interactive and zoomable web-based clustergrams.