Freenome Holdings, Inc.

Daniel Delubac

Adam Drake

Eric Ariazi

Imran Haque

Brandon White

Nathan Wan

Katherine Niehaus

Tzu-Yu Liu

Ajay Kannan

The invention provides for a system for sample extraction that includes (a) a sample extraction device comprising a tubular structure, at least one channel, a reservoir, a fluid flow unit, and at least one sensor; (b) a container; (c) a motion platform operatively coupled to the tubular structure; (d) an actuator operatively coupled to the motion platform; and (e) one or more computer processors operatively coupled to the at least one sensor, the actuator, and the fluid flow unit.

Methods and systems for sample extraction

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.

Machine learning implementation for multi-analyte assay development and testing

The present disclosure provides methods and devices for sample extraction.

Growing community of inventors

Leesburg, VA, United States of America

Daniel Delubac