CASE STUDY: Top 20 Pharma with a Focus on Rare Disease
Easy, comprehensive and data driven method to prioritize therapeutic area focus by mining the competitive landscape for rare disease groups, stratified based on molecular mechanisms.
Top 3 therapeutic focus areas arrived at in a few hours instead of weeks freeing up time to focus on the merits of pursuing each area.
A rational data driven approach to prioritize therapeutic areas.
An in-house person assigns disease classes.
Arbitrary, subjective and inconsistent - No one person can have in-depth knowledge about every disease
The analyst evaluates the competitive landscape by manually collecting Clinical Trials data for each disease in the class.
Laborious, time consuming and error prone
A web based tool allowing the analyst to build a view of the competitive landscape and identify white spaces to pursue
- Resolve diseases across data sets and varied taxonomies.
- Create a connected knowledge graph with disease data such as targets, pathways, tissue expression and trials from across sources.
- Stratify diseases using Machine Learning.
- Identify biologically relevant entities using Natural language processing to map data from structured and unstructured sources.
(c) Cerenode Inc. 2015-2020