Rave Omics

As part of the Medidata platform, Rave Omics is developed to work seamlessly with Rave Electronic Data Capture within the Medidata cloud infrastructure to address the challenges of including omic data in clinical trials.

Accelerate Timelines To Omics-Driven Safety and Efficacy Insights

Designed to enable the identification of actionable hypotheses for ongoing studies and to power future omics-based scientific findings across studies as a path to precision medicine, Rave Omics is an easy-to-use solution that improves and streamlines the process for every group in an organization that handles omic data.

Features and Benefits

Streamline Integration of Multi-Omic Data

  • Omic data management dashboards

  • Turnkey integration of clinical and omics data

  • Upload omic data asynchronously and securely into a single source of truth

Prevent Costly Mistakes

Automated detection of quality issues in omic data during the trial reduces risk and saves money by excluding erroneous data and allowing for the possibility of re-collecting those samples, Rave Omics can identify samples that are

  • Duplicate samples assigned to different subjects

  • Mismatched between the clinical dataset of a subject and the genomic sex of the samples

  • Outlier samples

Breakdown Silos

  • An easy-to-use interface and robust, reproducible analytics complement your biomarker discovery team

  • Rave Omics breaks down traditional barriers between computational and scientific staff viewing these data

Rave Omics drives scientific insights for patient benefit

Analytics to assist in identifying patients who will best respond to a therapeutic agent or those who are more likely to suffer from adverse events. Efficient access to well-established tools for biomarker discovery increases analysis throughput and leads to reproducible discoveries:

  • Unsupervised clustering: Cluster samples using expression data and detect associations to clinical variables
  • Tests of association: Explore your clinical and omic data and identify the strongest associations between clinical and omic variables.
  • Regression trees: Identify clinical or omic variables that predict time to even e.g., survival time) or that interact with the treatment effect

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