Evidence map›Paper›PMID 35241837›Full record

ArticleNature biotechnology2022

Designing sensitive viral diagnostics with machine learning.

Hayden C Metsky, Nicole L Welch, Priya P Pillai, Nicholas J Haradhvala, Laurie Rumker, Sreekar Mantena, Yibin B Zhang, David K Yang, Cheri M Ackerman, Juliane Weller and 4 more

Abstract read
In one paragraph

Article in Nature biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.

0numbers the graph read from it
0cells of the map it votes in
57citing 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

57 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Machine Learning for CRISPR-Based Diagnostics.International journal of molecular sciences · 2026
    Review
  9. Article
  10. CasCADE: CasMicrobiology spectrum · 2026
    Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Proteomics in the Diagnosis of Viral and Pathogenic Infections.Advances in experimental medicine and biology · 2026
    Review
  19. Review
  20. Review
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

14 authors.

Hayden C MetskyBroad Institute of MIT and Harvard, Cambridge, MA, USA. hayden@broadinstitute.org.ORCID http://orcid.org/0000-0002-8871-2349
Nicole L WelchBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Priya P PillaiBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-3760-2166
Nicholas J HaradhvalaBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-1113-3135
Laurie RumkerBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Sreekar MantenaBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-8834-1461
Yibin B ZhangBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-2546-6790
David K YangBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Cheri M AckermanBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Juliane WellerBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-1310-6168
Paul C BlaineyBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-4889-8783
Cameron MyhrvoldBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Michael MitzenmacherBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Pardis C SabetiBroad Institute of MIT and Harvard, Cambridge, MA, USA. pardis@broadinstitute.org.ORCID http://orcid.org/0000-0002-9843-1890

Funding

Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
MOLECULAR BIOPHYSICS TRAINING GRANTT32GM008313 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI MURTHY, VENKATESH N · 1989 to 2023
$14.6M
Computational Methods for Designing Optimal Genomics-guided Viral DiagnosticsK01AI163498 · NIAID · BROAD INSTITUTE, INC. · PI METSKY, HAYDEN C · 2021 to 2022
$232k
Discovery of compounds and genes that regulate cancer's epigenome, using combinatorial screening in a nanodrop-microwell platformF32CA236425 · NCI · BROAD INSTITUTE, INC. · PI ACKERMAN, CHERI MARIE · 2019 to 2020
$91k
Howard Hughes Medical InstituteNCI NIH HHS F32 CA236425NHGRI NIH HHS T32 HG002295NIAID NIH HHS K01 AI163498NIGMS NIH HHS T32 GM008313
6 · The paper itself

Abstract

Design of nucleic acid-based viral diagnostics typically follows heuristic rules and, to contend with viral variation, focuses on a genome's conserved regions. A design process could, instead, directly optimize diagnostic effectiveness using a learned model of sensitivity for targets and their variants. Toward that goal, we screen 19,209 diagnostic-target pairs, concentrated on CRISPR-based diagnostics, and train a deep neural network to accurately predict diagnostic readout. We join this model with combinatorial optimization to maximize sensitivity over the full spectrum of a virus's genomic variation. We introduce Activity-informed Design with All-inclusive Patrolling of Targets (ADAPT), a system for automated design, and use it to design diagnostics for 1,933 vertebrate-infecting viral species within 2 hours for most species and within 24 hours for all but three. We experimentally show that ADAPT's designs are sensitive and specific to the lineage level and permit lower limits of detection, across a virus's variation, than the outputs of standard design techniques. Our strategy could facilitate a proactive resource of assays for detecting pathogens.

Indexed as

Machine LearningNucleic AcidsNeural Networks, ComputerNucleic Acids

Identifiers

PMID35241837
PMCPMC9287178

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.