AI for Biomarker Discovery and Detection
Where machine learning genuinely accelerates biomarker science, and where it produces confident noise.
Why biomarkers are hard
High-dimensional omics, small cohorts and heterogeneous phenotypes make classical statistics fragile. Most candidate biomarkers fail to replicate, and the ones that survive rarely make it into a validated assay.
Where AI helps
Feature discovery across multi-omic data, integration of imaging with molecular signals, and prioritisation of candidates for wet-lab validation. Modern representation learning finds structure that univariate screens cannot, provided the training data is honest about batch, site and time.
Where it does not
A model that fits a cohort of 80 will fit anything. We insist on held-out validation, external cohorts where they exist, and mechanistic plausibility before a candidate advances. A p-value from a leaky pipeline is not a discovery.
From discovery to detection
A biomarker only matters once it can be measured reliably in a clinical lab. That is the bridge to BioLens: predictive QC, instrument health and result harmonisation across a fleet, so the signal found in research survives contact with operations.
The engineers, clinicians and analysts who build and run Good AI's platforms, writing about the work with the numbers we can stand behind.