FURI | Spring 2022
Boosting TCR and Epitope Binding Affinity via Supervised Contrastive Loss
In this paper, the research team investigates using Supervised Contrastive Loss (SCL) instead of Cross Entropy Loss (CEL) as the loss optimization function in existing machine learning models that are used to predict T-cell receptor (TCR) and Epitope binding affinity. The goal of SCL is to pull positive examples away from negative ones in latent space, before classification. The resulting process is one that trains a projection network using SCL, freezes the gradients of that network, and finally trains two dense layers on top of it using CEL for final classification.
Hometown: Glendale, Arizona, United States
Graduation date: Spring 2022