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Title: Pep-TCRNet: Prediction of Multi-Class Peptides by T-cell Receptor Sequences with Deep Learning
Pep-TCRNet is a novel approach to constructing a prediction model that can evaluate the probability of recognition between a TCR and a peptide amino acid sequence while combining inputs such as TCR sequences, HLA types, and VJ genes.Pep-TCRNet operates in two key steps:Feature Engineering: This step processes different types of variables:TCR and peptide amino acid sequencing data: The model incorporates neural network architectures inspired by language representation models and graph representation model to learn the meaningful embeddings.Categorical data: Specialized encoding techniques are used to ensure optimal feature representation for HLA types and VJ genes.Prediction Model: The second step involves training a prediction model to evaluate the likelihood of a TCR recognizing a specific peptide, based on the features generated in the first step.  more » « less
Award ID(s):
2137983
PAR ID:
10659496
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Publisher / Repository:
Oxford University Press
Date Published:
Subject(s) / Keyword(s):
Applications in health
Format(s):
Medium: X Size: 59344422 Bytes
Size(s):
59344422 Bytes
Right(s):
open access
Sponsoring Org:
National Science Foundation
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