Identifying active transcription factors and kinases from expression data using pathway queries

摘要: Motivation: Although progress has been made identifying regulatory relationships from expression data in general, only few methods have focused on detecting biological mechanisms like active pathways using a single measurement. This is of particular importance when measurements are available, e.g. if special cell types or conditions under investigation. Here we present method to test user specified hypotheses (pathway queries) where prior knowledge given the form networks and functional annotations. Based this method, develop scoring function identify transcription factors kinases, thus making first step toward explaining measured data. Results: We apply algorithm Rosetta Yeast Compendium dataset, finding that many cases results concordance with knowledge. were able confirm lesser degree, kinases identified by our play an important role processes affected respective knockouts. Furthermore, show correlation inferred activities can provide evidence for physical interaction cooperation plain fails do so. Contact: florian.sohler@bio.ifi.lmu.de