Predicting T-Cell Receptor Specificity

TL;DR

This paper introduces a Random Forest-based framework for predicting T-cell receptor specificity, improving screening efficiency and outperforming standard deep learning methods, with practical optimization suggestions.

Contribution

The study presents a novel TCR specificity prediction framework combining an antigen selector and a Random Forest classifier, enhancing accuracy over traditional deep learning models.

Findings

The Random Forest classifier outperforms ordinary deep learning methods.

The model effectively screens TCRs and target antigens.

Optimization suggestions address model shortcomings.

Abstract

Researching the specificity of TCR contributes to the development of immunotherapy and provides new opportunities and strategies for personalized cancer immunotherapy. Therefore, we established a TCR generative specificity detection framework consisting of an antigen selector and a TCR classifier based on the Random Forest algorithm, aiming to efficiently screen out TCRs and target antigens and achieve TCR specificity prediction. Furthermore, we used the k-fold validation method to compare the performance of our model with ordinary deep learning methods. The result proves that adding a classifier to the model based on the random forest algorithm is very effective, and our model generally outperforms ordinary deep learning methods. Moreover, we put forward feasible optimization suggestions for the shortcomings and challenges of our model found during model implementation.