The Challenge

Kidney transplant recipients still face high rejection rates, and clinicians need predictive diagnostic tests that can be used soon after surgery. Liquid biopsies based on RNA profiling generate vast amounts of complex data that are difficult to translate into actionable diagnostics. The client needed to transform this data into a fully automated test with regulatory compliance.

Our Approach

We combined data integration, algorithm development and end‑to‑end automation to turn complex RNA profiles into a fully automated diagnostic test.

• Data integration & biomarker discovery: Validated two academic studies on the client’s data and discovered novel biomarker panels for acute and chronic rejection.
• Algorithm development: Built AI models using the IDx/MDx platform and custom bioinformatics pipelines to predict rejection.
• End‑to‑end automation: Developed a fully automated pipeline with quality control, reporting and scalable cloud infrastructure validated to CLIA standards.

The Outcome

• Reduced sequencing costs through in‑silico simulation and streamlined processing to a five‑day pipeline.
• High test risk scores correlated with 83 % of early biopsies indicating acute rejection.
• Discovered new biomarker panels and provided a best‑in‑class, fully automated diagnostic test.
• Ongoing data‑driven algorithm refinement broadens applicability across patient populations.

Why It Matters

This case shows how combining biomarker discovery, AI modelling and automation can turn complex RNA profiling into a clinically validated diagnostic. The project improved cost efficiency and speed while delivering a transparent, scalable diagnostic that guides post‑transplant care.