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Scientific challenge

It is challenging to apply a systematic approach to identify the key characteristics that best define a cohort. Life scientists often know they are missing relevant cohort features. 

Teams need a structured environment to appropriately select relevant stratification criteria, perform cohort analyses and generate traceable results.

Comparison between different studies which do not follow the same standard often leads to invalid or incomplete results, owing to the lack of standardized contextual metadata.

e[cohort] generates reliable and reproducible results via unconstrained, data-driven virtual cohorts underpinned by the e[datascientist] valuation engine.

Key features

  • Create virtual cohorts of subjects based on multiple selection criteria (e.g. genotype, phenotype, indication, biomarker, treatment regime, etc.)
  • Apply and maintain (store, share, reuse) customizable valuation models to rank and prioritize virtual cohorts
  • Perform meta-analysis and downstream cohort analysis, using tools to help with network exploration, statistical models, subgroup comparisons and stratification
  • Generate interactive visualizations of data, and present results in the most compelling, informative way
  • Export comprehensive reports within minutes, ensuring traceability, transparency and collaboration
  • Build unconstrained virtual cohorts driven by a robust valuation framework
  • Define cohorts in a collaborative environment, ensuring informed stakeholder alignment
  • Evidence cohort priorities with justifiable criteria based on true data characteristics
  • Identify indicative correlations and use causal analysis to confirm/refute causal interactions
  • Inform future follow-up experimental and in silico studies to accelerate innovation

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Innovating for a better future

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