The explosive growth of genomics (microarrays, NGS, digital pathology, etc), metabolomics and proteomics data generated from research studies and clinical trials, coupled to huge amounts of patient data resulting from the digitization of healthcare, offers great promise for precision medicine.
Whether your organization is trying to understand cancer better, researching new therapies, searching for new biomarkers, repurposing existing drugs or stratifying patients for clinical trials, translating these massive volumes of omic and other data into meaningful scientific insights presents significant challenges.
Data complexity challenge—efficiently processing, managing, and analyzing complex omic and phenotypic datasets to answer clinically relevant questions
Distributed data challenge—accessing globally distributed datasets and efficiently harmonizing them into the data processing and analysis workflow to maximize scientific insights
Regulatory challenge—complying with data privacy and regulatory requirements while working with patient-related data in a research environment