Evidence map›Paper›PMID 34630352›Full record

ArticleFrontiers in microbiology2021

Application of Machine Learning Techniques to an Agent-Based Model of

Serena H Chen, Pablo Londoño-Larrea, Andrew Stephen McGough, Amber N Bible, Chathika Gunaratne, Pablo A Araujo-Granda, Jennifer L Morrell-Falvey, Debsindhu Bhowmik, Miguel Fuentes-Cabrera

Abstract read
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In one paragraph

Article in Frontiers in microbiology, 2021. 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

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.

Serena H ChenComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States.
Pablo Londoño-LarreaChemical Engineering Faculty, Universidad Central del Ecuador, Quito, Ecuador.
Andrew Stephen McGoughSchool of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.
Amber N BibleBiosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States.
Chathika GunaratneComputer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States.
Pablo A Araujo-GrandaChemical Engineering Faculty, Universidad Central del Ecuador, Quito, Ecuador.
Jennifer L Morrell-FalveyBiosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States.
Debsindhu BhowmikComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States.
Miguel Fuentes-CabreraCenter for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in

Indexed as

agent-based modelmachine learningneural networkPantoearandom forest regression

Identifiers

PMID34630352
PMCPMC8499321

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.