Potomac, MD, United States of America

Jeff Xiwu Zhou


Average Co-Inventor Count = 4.0

ph-index = 1

Forward Citations = 10(Granted Patents)


Company Filing History:


Years Active: 2014

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1 patent (USPTO):Explore Patents

Title: Jeff Xiwu Zhou: Innovator in Tumor Classification Technology

Introduction

Jeff Xiwu Zhou is a notable inventor based in Potomac, MD (US). He has made significant contributions to the field of cancer research, particularly in the development of advanced tumor classification technologies. His work focuses on utilizing artificial neural networks to enhance the accuracy of tumor detection and classification.

Latest Patents

Jeff Xiwu Zhou holds a patent for an innovative technology titled "Artificial Neural Network Proteomic Tumor Classification." This patent describes a tumor classifier based on protein expression. The invention discloses the use of proteomics to construct a highly accurate artificial neural network (ANN)-based classifier for detecting individual tumor types. It also distinguishes between six common tumor types in an unknown primary diagnosis setting. The classifier identifies discriminating sets of proteins that serve as biomarkers for six carcinomas. A leave-one-out cross-validation (LOOCV) method was employed to test the network's predictive ability, achieving a maximum predictive accuracy of 87% and an average predictive accuracy of 82% across the selected proteins.

Career Highlights

Throughout his career, Jeff Xiwu Zhou has worked with esteemed institutions such as the H. Lee Moffitt Cancer Center and Research Institute, Inc. and the University of South Florida. His experience in these organizations has contributed to his expertise in cancer research and technology development.

Collaborations

Jeff has collaborated with notable professionals in the field, including Timothy J. Yeatman and Gregory C. Bloom. These collaborations have further enriched his research and innovation efforts.

Conclusion

Jeff Xiwu Zhou is a pioneering inventor whose work in artificial neural networks and tumor classification is making a significant impact in cancer diagnostics. His contributions are paving the way for more accurate and efficient cancer detection methods.

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