FURI | Summer 2020
Towards Automated Identification of Cancer Immunotherapy Targets: Prediction of Binding Affinity of T Cell Receptor and Antigens Using Graph-Guided Deep Neural Network
Cancer can be treated through immunotherapy, a personalized treatment to train a person’s own body to create an immune response towards cancer cells. The problem arises in understanding when a cancer cell is recognizable to the body as harmful. The researcher created a graph-guided neural network to predict cellular bindings given major histocompatibility complexes of cells and T-cell receptors as pairs of data. When presented with the MHC from a particular patient, these predictions will aid immunotherapists in creating a treatment for that individual.
Hometown: Chandler, Arizona, United States
Graduation date: Fall 2020