The Business Problem

A major pharmaceutical company had two critical challenges: (1) leveraging genetic data from 500K patients to enable data-driven decision making, and (2) extracting associations from a large corpus of 20 million biomedical articles.

The genetic data comprised terabytes of patient information requiring advanced preprocessing, dimensionality reduction, and clinical integration. The literature corpus needed NLP models for text classification and entity extraction at scale.

The INM Consulting Approach

We developed an end-to-end Machine Learning workflow for genetic data analysis and NLP models for biomedical literature mining.

Implementation Details

• Parsed and preprocessed terabytes of patient genetic data
• Applied normalization, dimensionality reduction, and statistical tests to distinguish signal from noise
• Integrated genetic data with clinical data
• Performed supervised analysis to identify features that drive clinical response
• Built interactive visualizations and dashboards using Tibco Spotfire
• Developed interactive web apps using Streamlit, deployed using Nginx
• Built REST APIs using FastAPI
• Developed NLP model on 20 million biomedical articles for entity extraction and text classification

Technologies Used