ArticleProteomics2020
DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics.
Article in Proteomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.
- Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- pXg: Comprehensive and Reliable Identification of MHC-I Antigens Using De Novo Peptide Sequencing and Immunotranscriptomic Datasets.Methods in molecular biology (Clifton, N.J.) · 2027Article
- XL-MSDigger: a deep learning-based, versatile solution for cross-linking mass spectrometry.Nature communications · 2026Article
- Assessing the Performance of Mass Spectrometry Search Strategies in Identifying Translational Errors Using PDX Proteomics Data.Molecular & cellular proteomics : MCP · 2026Article
- Koina: Democratizing machine learning for proteomics research.Nature communications · 2025Article
- Enhancing peptide identification in metaproteomics through curriculum learning in deep learning.Nature communications · 2025Article
- Open-Source and FAIR Research Software for Proteomics.Journal of proteome research · 2025Review
- Validation and quantification of peptide antigens presented on MHCs using SureQuant.Nature protocols · 2025Review
- Maximizing Immunopeptidomics-Based Bacterial Epitope Discovery by Multiple Search Engines and Rescoring.Journal of proteome research · 2025Article
- TIMSJournal of proteome research · 2025Article
- Enhanced Discovery of Alternative Proteins (AltProts) in Mouse Cardiac Development Using Data-Independent Acquisition (DIA) Proteomics.Analytical chemistry · 2025Article
- Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines.Nature communications · 2024Article
- Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification.Molecular & cellular proteomics : MCP · 2024Review
- Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MSNature communications · 2024Article
- Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification.Molecular & cellular proteomics : MCP · 2024Article
- MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer.Nature communications · 2024Article
- The Breast Cancer Proteome and Precision Oncology.Cold Spring Harbor perspectives in medicine · 2023Review
- Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery.Journal for immunotherapy of cancer · 2023Review
- Real-Time Spectral Library Matching for Sample Multiplexed Quantitative Proteomics.Journal of proteome research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The identification of major histocompatibility complex (MHC)-binding peptides in mass spectrometry (MS)-based immunopeptideomics relies largely on database search engines developed for proteomics data analysis. However, because immunopeptidomics experiments do not involve enzymatic digestion at specific residues, an inflated search space leads to a high false positive rate and low sensitivity in peptide identification. In order to improve the sensitivity and reliability of peptide identification, a post-processing tool named DeepRescore is developed. DeepRescore combines peptide features derived from deep learning predictions, namely accurate retention timeand MS/MS spectra predictions, with previously used features to rescore peptide-spectrum matches. Using two public immunopeptidomics datasets, it is shown that rescoring by DeepRescore increases both the sensitivity and reliability of MHC-binding peptide and neoantigen identifications compared to existing methods. It is also shown that the performance improvement is, to a large extent, driven by the deep learning-derived features. DeepRescore is developed using NextFlow and Docker and is available at https://github.com/bzhanglab/DeepRescore.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.