Machine learning implementation for multi-analyte assay development and testing

US11681953B2 · kind B2 · utility

Assignee

• Freenome Holdings, Inc. · US

Inventors

• Adam Drake · Pacifica, US
• Daniel Delubac · Leesburg, US
• Katherine Niehaus · San Francisco, US
• Eric Ariazi · Madison, US
• Imran Saeedul Haque · San Francisco, US
• Tzu-Yu Liu · San Jose, US
• Nathan Wan · South San Francisco, US
• Ajay Kannan · Dublin, US
• Brandon White · South San Francisco, US

Key dates

• Filing date: Apr 15, 2019
• Grant date: Jun 20, 2023
• Expiry date: Apr 15, 2039

Classification

• Technology area (CPC Y): Emerging Cross-Sectional Technologies
• CPC primary: Y02A90/10
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values. The system inputs the feature vector into the machine learning model and obtains an output classification of whether the sample has a specified property.