What question did this study set out to answer?

To summarize recent technological developments in artificial intelligence and deep learning frameworks applied to neoantigen prediction for personalized cancer immunotherapy.

January 9, 2023Frontiers in OncologyOpen Access

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

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Authors

YCYu Cai RCRui Chen SGShenghan Gao

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Overview

Review demonstrates machine learning advancements for neoantigen prediction across multi-omics cancer data, highlighting integrated deep learning pipelines for personalized immunotherapy.

Key Points

To summarize recent technological developments in artificial intelligence and deep learning frameworks applied to neoantigen prediction for personalized cancer immunotherapy.
Reviewed standard computational pipelines spanning paired tumor-blood genetic variant calling, peptide-MHC binding affinity scoring, and T-cell receptor interaction modeling.
Evaluated feature extraction methods and multi-layer neural network architectures utilized across modern predictive platforms.
Deep learning and hybrid machine learning frameworks outperform conventional single-algorithm models in processing high-dimensional multi-omics data for neoantigen discovery.
Integrated computational pipelines enhance the characterization of tumor epitope immunogenicity and MHC class I and II binding affinities, though optimization across multi-omics datasets remains an ongoing challenge.

Cite This Study

Cai et al. (2023) studied this question.

synapsesocial.com/papers/6a7d64582d005ba2c41cd477 https://doi.org/10.3389/fonc.2022.1054231

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January 9, 2023Frontiers in OncologyOpen Access

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

View Full Paper

Ask AI

Bookmark

Authors

YCYu Cai RCRui Chen SGShenghan Gao

Discussion

Member takes

Overview

Review demonstrates machine learning advancements for neoantigen prediction across multi-omics cancer data, highlighting integrated deep learning pipelines for personalized immunotherapy.

Key Points

To summarize recent technological developments in artificial intelligence and deep learning frameworks applied to neoantigen prediction for personalized cancer immunotherapy.
Reviewed standard computational pipelines spanning paired tumor-blood genetic variant calling, peptide-MHC binding affinity scoring, and T-cell receptor interaction modeling.
Evaluated feature extraction methods and multi-layer neural network architectures utilized across modern predictive platforms.
Deep learning and hybrid machine learning frameworks outperform conventional single-algorithm models in processing high-dimensional multi-omics data for neoantigen discovery.
Integrated computational pipelines enhance the characterization of tumor epitope immunogenicity and MHC class I and II binding affinities, though optimization across multi-omics datasets remains an ongoing challenge.

Cite This Study

Cai et al. (2023) studied this question.

synapsesocial.com/papers/6a7d64582d005ba2c41cd477 https://doi.org/10.3389/fonc.2022.1054231

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