Company Filing History:
Years Active: 2023
Title: Imran Haque: Innovator in Machine Learning for Cancer Diagnostics
Introduction
Imran Haque is a prominent inventor based in South San Francisco, CA (US). He has made significant contributions to the field of medical diagnostics, particularly through the application of machine learning technologies. With a total of 2 patents, his work focuses on enhancing the accuracy and efficiency of cancer diagnostic tests.
Latest Patents
Haque's latest patent involves a groundbreaking system and method for analyzing blood-based cancer diagnostic tests using multiple classes of molecules. This innovative system employs machine learning (ML) to analyze various analytes, including cell-free DNA, cell-free microRNA, and circulating proteins from biological samples. By utilizing multiple assays such as whole-genome sequencing and quantitative immunoassay, the system aims to improve the sensitivity and specificity of diagnostics. 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 revolutionizing cancer diagnostics.
Career Highlights
Imran Haque is currently associated with Freenome Holdings, Inc., where he continues to push the boundaries of innovation in the healthcare sector. His expertise in machine learning and diagnostics has positioned him as a key player in the development of advanced medical technologies.
Collaborations
Haque collaborates with talented professionals in his field, including coworkers Adam Drake and Daniel Delubac. Their combined efforts contribute to the advancement of diagnostic methodologies and the integration of machine learning in healthcare.
Conclusion
Imran Haque's work exemplifies the intersection of technology and healthcare, particularly in the realm of cancer diagnostics. His innovative patents and collaborations highlight the importance of machine learning in improving diagnostic accuracy and patient outcomes.