Evidence map›Paper›PMID 31611695›Full record

ArticleNature biotechnology2019

Predicting HLA class II antigen presentation through integrated deep learning.

Binbin Chen, Michael S Khodadoust, Niclas Olsson, Lisa E Wagar, Ethan Fast, Chih Long Liu, Yagmur Muftuoglu, Brian J Sworder, Maximilian Diehn, Ronald Levy and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 185 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
185citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

185 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Pooled it
  4. Review
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  6. Therapeutic vaccines for NSCLC: From mechanisms to clinical translation.Chinese medical journal pulmonary and critical care medicine · 2026
    Review
  7. Review
  8. Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026
    Review
  9. Article
  10. Review
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  14. Article
  15. Article
  16. A PD-1/PD-L1-Sensitive Co-culture-Based Primary T-Cell Activation Assay.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  17. Article
  18. Review
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  20. Article

125 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Binbin ChenDepartment of Genetics, Stanford University, Stanford, CA, USA.
Michael S KhodadoustDepartment of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
Niclas OlssonDepartment of Chemical and Systems Biology, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-9746-0542
Lisa E WagarDepartment of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
Ethan FastDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Chih Long LiuDepartment of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
Yagmur MuftuogluStanford School of Medicine, Stanford University, Stanford, CA, USA.
Brian J SworderDepartment of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
Maximilian DiehnStanford Cancer Institute, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-2032-0581
Ronald LevyDepartment of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
Mark M DavisDepartment of Microbiology and Immunology, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-6868-657X
Joshua E EliasDepartment of Chemical and Systems Biology, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-8063-3259
Russ B AltmanDepartment of Genetics, Stanford University, Stanford, CA, USA.
Ash A AlizadehDepartment of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA. arasha@stanford.edu.ORCID http://orcid.org/0000-0002-5153-5625

Funding

Using a tonsil organoid system to probe conditions for the induction of protective antibody and T cell responses to influenza.U19AI057229 · NIAID · STANFORD UNIVERSITY · PI Mark Morris Davis · 2003 to 2026
$88.5M
Modeling the Molecular Determinants of Induced Anti-Tumor Immune Responses in Mantle Cell LymphomaU01CA194389 · NCI · STANFORD UNIVERSITY · PI ALIZADEH, ASH ARASH, DAVIS, MARK MORRIS · 2015 to 2019
$3.1M
Undergraduate Summer Research Experiences Support for Combining systems biology and structural biology to find new therapeuticsR01GM102365 · NIGMS · STANFORD UNIVERSITY · PI ALTMAN, RUSS BIAGIO · 2012 to 2021
$3.0M
Immune Targeting of non-Hodgkin Lymphoma through Integrative Antigen Presentation ProfilingK08CA207882 · NCI · STANFORD UNIVERSITY · PI KHODADOUST, MICHAEL SIAVASH · 2018 to 2022
$868k
BD Canto II RUO CytometerS10RR027431 · NCRR · STANFORD UNIVERSITY · PI NOLAN, GARRY P · 2010 to 2010
$236k
NCI NIH HHS K08 CA207882NCI NIH HHS U01 CA194389NCRR NIH HHS S10 RR027431NIAID NIH HHS U19 AI057229NIGMS NIH HHS R01 GM102365
6 · The paper itself

Abstract

Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules would be valuable for vaccine development and cancer immunotherapies. Current computational methods trained on in vitro binding data are limited by insufficient training data and algorithmic constraints. Here we describe MARIA (major histocompatibility complex analysis with recurrent integrated architecture; https://maria.stanford.edu/ ), a multimodal recurrent neural network for predicting the likelihood of antigen presentation from a gene of interest in the context of specific HLA class II alleles. In addition to in vitro binding measurements, MARIA is trained on peptide HLA ligand sequences identified by mass spectrometry, expression levels of antigen genes and protease cleavage signatures. Because it leverages these diverse training data and our improved machine learning framework, MARIA (area under the curve = 0.89-0.92) outperformed existing methods in validation datasets. Across independent cancer neoantigen studies, peptides with high MARIA scores are more likely to elicit strong CD4

Indexed as

Antigen PresentationCD4-Positive T-LymphocytesComputational BiologyDeep LearningHistocompatibility Antigens Class IIHumansK562 CellsMass SpectrometryNeural Networks, ComputerPeptidesSequence Analysis, RNAHistocompatibility Antigens Class IIPeptides

Identifiers

PMID31611695
PMCPMC7075463

What OpenQuestion holds

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Registered trials

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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.