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.