Evidence map›Paper›PMID 37821700›Full record

ArticleNature2023

Learning from prepandemic data to forecast viral escape.

Nicole N Thadani, Sarah Gurev, Pascal Notin, Noor Youssef, Nathan J Rollins, Daniel Ritter, Chris Sander, Yarin Gal, Debora S Marks

Abstract read
In one paragraph

Article in Nature, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers.

0numbers the graph read from it
0cells of the map it votes in
133citing papers in PubMed
–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

133 citing papers in PubMed.

  1. Global Genomic Surveillance.Methods in molecular biology (Clifton, N.J.) · 2027
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73 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Nicole N Thadani *Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
Sarah Gurev *Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-5931-6622
Pascal Notin *OATML Group, Department of Computer Science, University of Oxford, Oxford, UK.
Noor YoussefMarks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
Nathan J RollinsMarks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
Daniel RitterMarks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.ORCID 0009-0009-3266-9917
Chris SanderMarks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
Yarin GalOATML Group, Department of Computer Science, University of Oxford, Oxford, UK.ORCID 0000-0002-2733-2078
Debora S MarksMarks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA. debbie@hms.harvard.edu.ORCID 0000-0001-9388-2281

Funding

Making antibody generation rapid, scalable, and democratic through machine learning and continuous evolutionR01CA260415 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI KRUSE, ANDREW, LIU, CHANG C · 2020 to 2024
$8.4M
Deep learning to design immune-evading viral vectors for gene therapyF32GM141007 · NIGMS · HARVARD MEDICAL SCHOOL · PI THADANI, NICOLE · 2021 to 2021
$66k
NCI NIH HHS R01 CA260415NIGMS NIH HHS F32 GM141007
6 · The paper itself

Abstract

Effective pandemic preparedness relies on anticipating viral mutations that are able to evade host immune responses to facilitate vaccine and therapeutic design. However, current strategies for viral evolution prediction are not available early in a pandemic-experimental approaches require host polyclonal antibodies to test against

Indexed as

Evolution, MolecularForecastingImmune EvasionMutationPandemicsVirusesDrug DesignHIV InfectionsHumansInfluenza, HumanLassa virusNipah VirusSARS-CoV-2Viral VaccinesViral Vaccines

Identifiers

PMID37821700
PMCPMC10599991

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.