Evidence map›Paper›PMID 39990353›Full record

ArticlebioRxiv : the preprint server for biology2025

Craft: A Machine Learning Approach to Dengue Subtyping.

Daniel J van Zyl, Marcel Dunaiski, Houriiyah Tegally, Cheryl Baxter, INFORM Africa research study group, Tulio de Oliveira, Joicymara S Xavier

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Daniel J van ZylCentre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch University,South Africa.
Marcel DunaiskiComputer Science Division, Department of Mathematical Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, South Africa.
Houriiyah TegallyCentre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch University,South Africa.
Cheryl BaxterCentre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch University,South Africa.
INFORM Africa research study group
Tulio de OliveiraCentre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch University,South Africa.
Joicymara S XavierCentre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch University,South Africa.

Funding

eLwazi Open Science Data Platform for AfricaU2CEB032224 · NIBIB · UNIVERSITY OF CAPE TOWN · PI MULDER, NICOLA, SKELTON, MICHELLE · 2021 to 2025
$11.2M
Role of Data Streams In Informing Infection Dynamics in Africa- INFORM AfricaU54TW012041 · FIC · INSTITUTE OF HUMAN VIROLOGY · PI Alash'le G. Abimiku, Tulio de Paiva Nazareth Andrade De Oliveira · 2021 to 2026
$6.9M
FIC NIH HHS U54 TW012041NIBIB NIH HHS U2C EB032224
6 · The paper itself

Abstract

Motivation: The dengue virus poses a major global health threat, with nearly 390 million infections annually. A recently proposed hierarchical dengue nomenclature system enhances spatial resolution by defining major and minor lineages within genotypes, aiding efforts to track viral evolution. While current subtyping tools - Genome Detective, GLUE, and NextClade - rely on computationally intensive sequence alignment and phylogenetic inference, machine learning presents a promising alternative for achieving accurate and rapid classification. Results: We present Craft (

Identifiers

PMID39990353
PMCPMC11844389

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.