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Paper 2410.14127

by Eli N. Weinstein arxiv-paper--2410.14127
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0.0 Top 18%
S: Semantic 50
A: Authority 0
P: Popularity 0
R: Recency 0
Q: Quality 0
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A central question in human immunology is how a patient's repertoire of T cells impacts disease. Here, we introduce a method to infer the causal effects of T cell receptor (TCR) sequences on patient outcomes using observational TCR repertoire sequencing data and clinical outcomes data. Our approach corrects for unobserved confounders, such as a patient's environment and life history, by using the patient's immature, pre-selection TCR repertoire. The pre-selection repertoire can be estimated f...

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BibTeX
@misc{arxiv_paper__2410.14127,
  author = {Eli N. Weinstein},
  title = {Paper 2410.14127 Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2410.14127v1}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
Eli N. Weinstein. (2026). Paper 2410.14127 [Paper]. Free2AITools. https://arxiv.org/abs/2410.14127v1

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TOP 18% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 0
Recency (R) 0
Quality (Q) 0

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📝 Executive Summary

"A central question in human immunology is how a patient's repertoire of T cells impacts disease. Here, we introduce a method to infer the causal effects of T cell receptor (TCR) sequences on patient outcomes using observational TCR repertoire sequencing data and clinical outcomes data. Our approach corrects for unobserved confounders, such as a patient's environment and life history, by using the patient's immature, pre-selection TCR repertoire. The pre-selection repertoire can be estimated f..."

❝ Cite Node

@article{Weinstein2024ArXiv,
  title={ArXiv 2410.14127 Technical Profile},
  author={Eli N. Weinstein and Elizabeth B. Wood and David M. Blei},
  journal={arXiv preprint arXiv:arxiv-paper--2410.14127},
  year={2024}
}

đŸ‘Ĩ Collaborating Minds

Eli N. Weinstein Elizabeth B. Wood David M. Blei

Abstract & Analysis

A central question in human immunology is how a patient's repertoire of T cells impacts disease. Here, we introduce a method to infer the causal effects of T cell receptor (TCR) sequences on patient outcomes using observational TCR repertoire sequencing data and clinical outcomes data. Our approach corrects for unobserved confounders, such as a patient's environment and life history, by using the patient's immature, pre-selection TCR repertoire. The pre-selection repertoire can be estimated from nonproductive TCR data, which is widely available. It is generated by a randomized mutational process, V(D)J recombination, which provides a natural experiment. We show formally how to use the pre-selection repertoire to draw causal inferences, and develop a scalable neural-network estimator for our identification formula. Our method produces an estimate of the effect of interventions that add a specific TCR sequence to patient repertoires. As a demonstration, we use it to analyze the effects of TCRs on COVID-19 severity, uncovering potentially therapeutic TCRs that are (1) observed in patients, (2) bind SARS-CoV-2 antigens in vitro and (3) have strong positive effects on clinical outcomes.

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id
arxiv-paper--2410.14127
author
Eli N. Weinstein
tags
arxiv:stat.MLarxiv:cs.LGarxiv:q-bio.GN

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