id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-271849-wxmr8eki	Meysman, Pieter	Tracking SARS-CoV-2 T cells with epitope-T-cell receptor recognition models	2020-09-09		.txt	text/plain	2924	166	58	In this paper, we demonstrate the use of machine learning to classify SARS-CoV-2 epitope specific T-cell clonotypes in T-cell receptor (TCR) sequencing data. We apply these models to public TCR data and show how they can be used to study T-cell longitudinal profiles in COVID-19 patients to characterize how the adaptive immune system reacts to the SARS-CoV-2 virus. No other epitopes present in TCRex (including the 49 non-SARS-CoV-2 models) were predicted to have a single TCR target within this data set. Once established, these models can be applied to any TCR repertoire data and thus can be used to study putative SARS-CoV-2 reactive T cells in the currently available COVID-19 data. In addition, using such models on longitudinal data reveals a potential difference in temporal dynamics between T cells predicted to react against epitopes that are unique to SARS-CoV-2 and those that are shared among other coronaviruses.	./cache/cord-271849-wxmr8eki.txt	./txt/cord-271849-wxmr8eki.txt