Actionable predictive signatures from public and proprietary data
Drug development in oncology stands to benefit greatly from an increasing number of public datasets like The Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia (CCLE). Further, translational research groups and even pharma teams themselves regularly publish novel gene signatures that claim to improve our predictive, prognostic or diagnostic capabilities around a particular disease, mechanism or class of drug. But making use of these data resources and prior analytic works is not trivial. One must take a cautious and systematic approach to QA/QC, and spend the time understanding both the data and the models before hoping to apply them to new therapies and patient cohorts. Genialis is working with several leading biopharma, across a range of cancer sites and drug types, to leverage public and proprietary data in building predictive models for the next generation of life saving treatments.
Get the case study here: http://genial.is/predictive-signatures