Evidence map›Paper›PMID 39604453›Full record

ArticleNPJ systems biology and applications2024

A deep learning approach predicting the activity of COVID-19 therapeutics and vaccines against emerging variants.

Robert P Matson, Isin Y Comba, Eli Silvert, Michiel J M Niesen, Karthik Murugadoss, Dhruti Patwardhan, Rohit Suratekar, Elizabeth-Grace Goel, Brittany J Poelaert, Kanny K Wan and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ systems biology and applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Robert P Matson *nference, Cambridge, MA, 02139, USA.ORCID 0000-0003-4241-0641
Isin Y Comba *nference, Cambridge, MA, 02139, USA.
Eli Silvert *nference, Cambridge, MA, 02139, USA.
Michiel J M Niesen *nference, Cambridge, MA, 02139, USA. mniesen@nference.net.ORCID 0000-0002-9255-6203
Karthik Murugadossnference, Cambridge, MA, 02139, USA.
Dhruti Patwardhannference Labs, Bengaluru, Karnataka, 560017, India.
Rohit Suratekarnference Labs, Bengaluru, Karnataka, 560017, India.ORCID 0000-0002-6281-5526
Elizabeth-Grace Goelnference, Cambridge, MA, 02139, USA.
Brittany J PoelaertDivision of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA.
Kanny K WanDivision of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA.
Kyle R BrimacombeDivision of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA.ORCID 0000-0002-9629-1417
A J Venkatakrishnannference, Cambridge, MA, 02139, USA.
Venky Soundararajannference, Cambridge, MA, 02139, USA. venky@nference.net.ORCID 0000-0001-7434-9211

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding which viral variants evade neutralization is crucial for improving antibody-based treatments, especially with rapidly evolving viruses like SARS-CoV-2. Yet, conventional assays are labor intensive and cannot capture the full spectrum of variants. We present a deep learning approach to predict changes in neutralizing antibody activity of COVID-19 therapeutics and vaccine-elicited sera/plasma against emerging viral variants. Our approach leverages data of 67,885 unique SARS-CoV-2 Spike sequences and 7,069 in vitro assays. The resulting model accurately predicted fold changes in neutralizing activity (R

Indexed as

Antibodies, NeutralizingAntibodies, ViralCOVID-19COVID-19 VaccinesDeep LearningSARS-CoV-2Spike Glycoprotein, CoronavirusCOVID-19 Drug TreatmentHumansAntibodies, NeutralizingAntibodies, ViralCOVID-19 VaccinesSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2

Identifiers

PMID39604453
PMCPMC11603192

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.