Evidence map›Paper›PMID 38257767›Full record

ArticleViruses2023

Reclassification of ASFV into 7 Biotypes Using Unsupervised Machine Learning.

Mark Dinhobl, Edward Spinard, Nicolas Tesler, Hillary Birtley, Anthony Signore, Aruna Ambagala, Charles Masembe, Manuel V Borca, Douglas P Gladue

Open access · goldAbstract read
In one paragraph

Article in Viruses, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
9.0field-weighted citation impact, top 2% of its field
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

26 citing papers in PubMed, 26 citations in OpenAlex.

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  16. African swine fever virus biotype identification tool.Microbiology resource announcements · 2025
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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

9 authors at 4 institutions in 3 countries.

Mark DinhoblUnited States Department of Agriculture, Agricultural Research Service, Foreign Animal Disease Research Unit, Plum Island Animal Disease Center, Orient, NY 11957, USA.
Edward SpinardUnited States Department of Agriculture, Agricultural Research Service, Foreign Animal Disease Research Unit, Plum Island Animal Disease Center, Orient, NY 11957, USA.ORCID 0000-0001-7268-6580
Nicolas TeslerUnited States Department of Agriculture, Agricultural Research Service, Foreign Animal Disease Research Unit, Plum Island Animal Disease Center, Orient, NY 11957, USA.ORCID 0000-0001-8863-9260
Hillary BirtleyUnited States Department of Agriculture, Agricultural Research Service, Foreign Animal Disease Research Unit, Plum Island Animal Disease Center, Orient, NY 11957, USA.
Anthony SignoreCenter of Excellence for African Swine Fever Genomics, Guilford, CT 06437, USA.ORCID 0000-0002-0664-0864
Aruna AmbagalaCenter of Excellence for African Swine Fever Genomics, Guilford, CT 06437, USA.ORCID 0000-0003-1783-2105
Charles MasembeCenter of Excellence for African Swine Fever Genomics, Guilford, CT 06437, USA.ORCID 0000-0002-9581-0414
Manuel V BorcaUnited States Department of Agriculture, Agricultural Research Service, Foreign Animal Disease Research Unit, Plum Island Animal Disease Center, Orient, NY 11957, USA.
Douglas P GladueUnited States Department of Agriculture, Agricultural Research Service, Foreign Animal Disease Research Unit, Plum Island Animal Disease Center, Orient, NY 11957, USA.ORCID 0000-0002-7894-0233
Agricultural Research Service · USCanadian Food Inspection Agency · CAOak Ridge Associated Universities · USMakerere University · UG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In 2007, an outbreak of African swine fever (ASF), a deadly disease of domestic swine and wild boar caused by the African swine fever virus (ASFV), occurred in Georgia and has since spread globally. Historically, ASFV was classified into 25 different genotypes. However, a newly proposed system recategorized all ASFV isolates into 6 genotypes exclusively using the predicted protein sequences of p72. However, ASFV has a large genome that encodes between 150-200 genes, and classifications using a single gene are insufficient and misleading, as strains encoding an identical p72 often have significant mutations in other areas of the genome. We present here a new classification of ASFV based on comparisons performed considering the entire encoded proteome. A curated database consisting of the protein sequences predicted to be encoded by 220 reannotated ASFV genomes was analyzed for similarity between homologous protein sequences. Weights were applied to the protein identity matrices and averaged to generate a genome-genome identity matrix that was then analyzed by an unsupervised machine learning algorithm, DBSCAN, to separate the genomes into distinct clusters. We conclude that all available ASFV genomes can be classified into 7 distinct biotypes.

Indexed as

African Swine FeverAfrican Swine Fever VirusAlgorithmsAnimalsGenotypeSwineUnsupervised Machine LearningAfrican swine feverASFVbiotypeclassificationgenotype

Identifiers

PMID38257767
PMCPMC10819123
OpenAlexW4390467835

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.