The Business Problem

A biotech startup needed to identify novel drug targets from vast amounts of genomic, proteomic, and clinical data. Traditional bioinformatics approaches were time-consuming and couldn't integrate diverse data types effectively.

The challenge was to build a machine learning platform that could analyze multi-omics data, identify potential therapeutic targets, and prioritize them based on druggability, disease relevance, and commercial potential.

The INM Consulting Approach

We designed and implemented a comprehensive ML platform that integrated multi-omics data, applied advanced machine learning algorithms, and provided an intuitive interface for target exploration and prioritization.

Key Features

• Multi-omics data integration pipeline
• Feature engineering for biological data
• Machine learning models for target scoring
• Network analysis for pathway identification
• Interactive visualization and exploration tools

Technologies Used