Evidence map›Paper›PMID 40467887›Full record

ArticleBioprocess and biosystems engineering2025

Machine learning-optimized bioprocess for macroidin production by Lysinibacillus macroides and its biomedical applications.

Maurice George Ekpenyong, Philomena Effiom Edet, Atim David Asitok, Andrew Nosakhare Amenaghawon, Stanley Aimhanesi Eshiemogie, David Sam Ubi, Cecilia Uke Echa, Heri Septya Kusuma, Sylvester Peter Antai

Abstract read
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In one paragraph

Article in Bioprocess and biosystems engineering, 2025. 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

9 authors.

Maurice George EkpenyongEnvironmental Microbiology and Biotechnology Unit, Department of Microbiology, Faculty of Biological Sciences, University of Calabar, Calabar, Nigeria. mauriceekpenyong@unical.edu.ng.ORCID http://orcid.org/0000-0001-9601-5546
Philomena Effiom EdetEnvironmental Microbiology and Biotechnology Unit, Department of Microbiology, Faculty of Biological Sciences, University of Calabar, Calabar, Nigeria.
Atim David AsitokEnvironmental Microbiology and Biotechnology Unit, Department of Microbiology, Faculty of Biological Sciences, University of Calabar, Calabar, Nigeria.
Andrew Nosakhare AmenaghawonBioresources Valorization Laboratory, Department of Chemical Engineering, Faculty of Engineering, University of Benin, Benin-City, Nigeria.
Stanley Aimhanesi EshiemogieBioresources Valorization Laboratory, Department of Chemical Engineering, Faculty of Engineering, University of Benin, Benin-City, Nigeria.
David Sam UbiEnvironmental Microbiology and Biotechnology Unit, Department of Microbiology, Faculty of Biological Sciences, University of Calabar, Calabar, Nigeria.ORCID http://orcid.org/0000-0002-1773-070X
Cecilia Uke EchaFood and Industrial Microbiology Unit, Department of Microbiology, Faculty of Biological Sciences, University of Calabar, Calabar, Nigeria.ORCID http://orcid.org/0000-0001-9813-1102
Heri Septya KusumaDepartment of Chemical Engineering, Faculty of Industrial Technology, Universitas Pembangunan Nasional "Veteran" Yogyakarta, Yogyakarta, Indonesia.
Sylvester Peter AntaiEnvironmental Microbiology and Biotechnology Unit, Department of Microbiology, Faculty of Biological Sciences, University of Calabar, Calabar, Nigeria.ORCID http://orcid.org/0000-0002-2196-471X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The quest for solutions to infectious diseases and life-debilitating disease states has been ongoing for centuries now. Natural products researches have revealed bioactive compounds of plant and microbial origin that offer solutions to health conditions but with poor yield. This study reports yield improvement of a novel macroidin bacteriocin through robust comparative process optimization involving statistical and machine learning approaches. Response surface methodology (RSM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) models showed adequate fitting capabilities considering statistical indices and performance errors as: RSM (R

Indexed as

Anti-Bacterial AgentsBacillaceaeBacteriocinsMachine LearningHumansNeural Networks, ComputerAnti-Bacterial AgentsBacteriocinsBacteriocinBiomedical applicationsLysinibacillus macroidesMachine learning modelingProcess optimization

Identifiers

PMID40467887

What OpenQuestion holds

Textmetadata
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.