GGNpTCR: A Generative Graph Structure Neural Network for Predicting Immunogenic Peptides for T-cell Immune Response

Zhao, Minghua; Xu, Steven X.; Yang, Yaning; Yuan, Min

doi:10.1021/acs.jcim.3c01293

J. Chem. Inf. Model.

2023

DOI: 10.1021/acs.jcim.3c01293

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GGNpTCR: A Generative Graph Structure Neural Network for Predicting Immunogenic Peptides for T-cell Immune Response

Minghua Zhao,

Steven X. Xu,

Yaning Yang

et al.

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2024

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Cited by 3 publications

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Cracking the code of adaptive immunity: The role of computational tools

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Cracking the code of adaptive immunity: The role of computational tools

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2024

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T-cell receptor binding prediction: A machine learning revolution

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Harmonizing immune cell sequences for computational analysis with large language models

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Biology Methods and Protocols

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We present SEQuence Weighted Alignment for Sorting and Harmonization (Seqwash), an algorithm designed to process sequencing profiles utilizing large language models (LLMs). Seqwash harmonizes immune cell sequences into a unified representation, empowering LLMs to embed meaningful patterns while eliminating irrelevant information. Evaluation using immune cell sequencing data showcases Seqwash’s efficacy in standardizing profiles, leading to improved fea- ture quality and enhanced performance in both supervised and unsupervised downstream tasks for sequencing data.

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GGNpTCR: A Generative Graph Structure Neural Network for Predicting Immunogenic Peptides for T-cell Immune Response

Cited by 3 publications

References 44 publications

Cracking the code of adaptive immunity: The role of computational tools

Cracking the code of adaptive immunity: The role of computational tools

T-cell receptor binding prediction: A machine learning revolution

Harmonizing immune cell sequences for computational analysis with large language models

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