Rave Omics

Rave Omics

Use omic data for efficacy and safety decisions during and after your trial

Omic data and biomarkers are becoming ever more important in life science research. But managing all that data, and aligning it with clinical systems, can bog teams down instead of fueling progress. Enter Rave Omics, a first-in-class solution within the Medidata platform that brings quality omic data together with clinical EDC data and applies machine learning algorithms to drive  timely decisions on efficacy and safety during the conduct of a trial and insights across one or more trials, streamlining the integration and analysis of omic data to accelerate discovery of novel biomarkers.

Generate and test hypotheses earlier with high value analytics and immediately actionable omic data.

Reduce time and effort

Easily and efficiently ingest analysis-ready multi-omic data and other clinical study data onto the secure Medidata platform. Rave Omics enables simplified data management and integration as early as during trial conduct. This greatly reduces time intensive cross-team efforts downstream to access and harmonize different data types.

Earlier quality checks, fewer errors

Reduce risk and prevent costly mistakes with automated detection of omic and clinical data quality issues during conduct of the trial – not after. With Rave Omics, erroneous or mismatched data is discovered through earlier quality control checks while the subject is still enrolled, allowing time to address issues and, if necessary, sample recollection.

Robust analytics for biomarker discovery

Our suite of robust, automated and reproducible analytics is designed to identify correlations between omics-defined patient subsets and safety or efficacy outcomes. These capabilities bridge the efforts of computational and scientific staff, as well as those of research and clinical development overall. Rave Omics provides increased computational capacity through automation of high value analytics and accelerated hypothesis generation for biomarker discovery.

Less time managing data means more time for identifying novel biomarkers and improving patients’ lives.