2023 157 CSM

Cell Type Prediction with Advanced Machine Learning Approaches Using Single-Cell T-Cell Receptor Repertoire Sequencing Data

By: Michael Brennan

Department: Mathematics 

Faculty Advisor: Dr. Tao He

This research project aims to investigate the use of advanced machine learning approaches to predict cell type based on T-cell receptor (TCR) sequence data obtained through single-cell TCR repertoire sequencing. The TCR is a crucial component of the immune system that recognizes and responds to foreign antigens, and variations in TCR sequences are associated with specific cell types. Through the use of machine learning algorithms, this project seeks to accurately predict cell types based on TCR sequence data, which can have implications in the diagnosis and treatment of diseases. This research has the potential to help improve our understanding of the immune system and its role in health and disease.