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Optimizing Causal Drug Target and Marker Discovery with the GNS Supercomputer-Driven REFSâ„¢ Platform
Iya Khalil (Gene Network Sciences)
We have developed the ability to accurately predict disease outcomes and identify targets and markers on an individual basis by reverse-engineering and simulating in silico network disease models reflecting the causal connections between genetic variation, biological processes (as measured by molecular profiling) and outcomes, using our REFS™ modeling and simulation platform. We will present a case study in which we were able to discover both novel and known drug target analogs. We also were able to accurately predict out-of-sample mouse outcomes in dyslipidemia by simulating the out-of-sample mice’s genotypes in a model constructed from genetic variation, liver gene expression and lipid level data. We will also discuss current and future applications of these approaches in human data for target and marker discovery, and the prediction of patient-specific outcomes.
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