Rave Omics

Rave Omics with Biomarker Discovery

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

Omic data and biomarkers are becoming increasingly important in life science research. But managing all that data and aligning it with clinical systems can bog you down. Enter Rave Omics with Biomarker Discovery, a first-in-class solution within the Medidata Rave Clinical Cloud platform. It 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 the discovery of novel biomarkers.

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

Why Rave Omics?

Reduce Time and Effort

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

Reduce Errors with Earlier Quality Checks

Reduce risk and prevent costly mistakes with automated detection of omic and clinical data quality issues during the 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.

Get Robust Analytics for Biomarker Discovery

Our suite of robust, automated, and reproducible analytics are 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.

Spend less time managing data and more time identifying novel biomarkers and improving patients’ lives.