Machine-Learned Biomarker Discovery from Standard-of-Care Data

The patent introduces machine learning models as an intermediary that bridges the gap between standard-of-care data and predictive biomarker discovery. The ML models process routine clinical data (imaging, lab results, demographics) to infer molecular analyte activity, enabling robust biomarker discovery without requiring specialized assays or large dedicated cohorts. This intermediary approach allows the system to extract meaningful biomarker signals from existing SoC data, resolving the contradiction between measurement precision and sample size requirements.

The patent leverages inexpensive, routinely collected standard-of-care data (medical images, lab results, demographics) as substitute proxies for complex molecular assays. Instead of implementing new specialized assays, the system uses readily available SoC data that can be processed by machine learning models to infer molecular analyte activity. This approach replaces complex, costly assays with simple, abundant data sources, dramatically reducing implementation complexity while maintaining biomarker detection precision.

The patent enables the system to serve itself by using machine learning models to extract molecular analyte activity information from existing standard-of-care data collections. Rather than requiring separate robust data collection infrastructure for biological measurements, the system processes routinely collected SoC data through ML algorithms that automatically infer molecular activity patterns. This self-service approach eliminates the need for additional robust data collection systems while maintaining measurement precision, as the ML models learn to extract meaningful signals from the inherent variability in SoC data.