Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines

peer-reviewed · Nature Machine Intelligence · 2020

peer-reviewed · Nature Machine Intelligence · 2020. Ngoc Hieu Tran et al.

Date	2020-11-16
Type	peer-reviewed
Venue	Nature Machine Intelligence
Publisher	Nature Machine Intelligence
Contribution	downstream-application
DOI	10.1038/s42256-020-00260-4
Citations (OpenAlex)	42
Venue 2-year citedness	19.94

Preprint version: Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines (2019-04-26, bioRxiv)

Authors

1. Ngoc Hieu Tran · Bioinformatics Solutions Inc., University of Waterloo
2. Rui Qiao · Bioinformatics Solutions Inc., University of Waterloo
3. Lei Xin · Bioinformatics Solutions Inc.
4. Xin Chen · Bioinformatics Solutions Inc.
5. Baozhen Shan · Bioinformatics Solutions Inc.
6. Ming Li · Bioinformatics Solutions Inc., Peng Cheng Laboratory, University of Waterloo, University of Western Ontario

Methods and tools

• DeepNovoAA: Immunopeptidome neoantigen discovery

Cites (3)

• Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry (2018) crossref
• De novo peptide sequencing by deep learning (2017) crossref
• PEAKS DB: De Novo Sequencing Assisted Database Search for Sensitive and Accurate Peptide Identification (2012) crossref

Cited by (9)

• DiNovo: high-coverage, high-confidence de novo peptide sequencing using mirror proteases and deep learning (2025) crossref
• A transformer-based semi-autoregressive framework for high-speed and accurate de novo peptide sequencing (2025) both
• Deep Learning Methods for De Novo Peptide Sequencing (2024) crossref
• Bridging the Gap between Database Search and De Novo Peptide Sequencing with SearchNovo (2024) both
• NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics (2024) semanticscholar
• MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer (2024) semanticscholar
• Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model (2023) both
• BiATNovo: A Self-Attention based Bidirectional Peptide Sequencing Method (2023) both
• Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices (2021) crossref

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