South San Francisco, CA, United States of America

Katherine Niehaus

USPTO Granted Patents = 2 

Average Co-Inventor Count = 9.0

ph-index = 2

Forward Citations = 13(Granted Patents)


Company Filing History:


Years Active: 2023

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2 patents (USPTO):Explore Patents

Title: Katherine Niehaus: Innovator in Machine Learning for Cancer Diagnostics

Introduction

Katherine Niehaus is a prominent inventor based in South San Francisco, CA (US). She has made significant contributions to the field of machine learning, particularly in the development of diagnostic systems for cancer detection. With a total of 2 patents, her work is at the forefront of integrating advanced technology into healthcare.

Latest Patents

Katherine's latest patent focuses on the implementation of machine learning for multi-analyte assay development and testing. This innovative system analyzes blood-based cancer diagnostic tests using various classes of molecules, including cell-free DNA, cell-free microRNA, and circulating proteins. By employing machine learning (ML), the system enhances the sensitivity and specificity of diagnostics by utilizing independent information from multiple signals. The process involves receiving a biological sample, separating different molecule classes, and identifying feature sets for input into a machine learning model. Ultimately, the system classifies whether the sample possesses a specified property, showcasing the potential of ML in improving cancer diagnostics.

Career Highlights

Katherine is currently employed at Freenome Holdings, Inc., where she continues to push the boundaries of innovation in medical diagnostics. Her work is instrumental in developing systems that can significantly impact patient outcomes through early detection and accurate diagnosis.

Collaborations

Katherine collaborates with talented individuals such as Adam Drake and Daniel Delubac, contributing to a dynamic team focused on advancing healthcare technology.

Conclusion

Katherine Niehaus exemplifies the spirit of innovation in the field of cancer diagnostics through her groundbreaking work in machine learning. Her contributions are paving the way for more effective diagnostic tools that can ultimately save lives.

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