Evidence map›Paper›PMID 40930281›Full record

ReviewBioresource technology2026

Leveraging artificial intelligence for efficient microbial production.

Xinyu Gong, Jianli Zhang, Qi Gan, Wei Ruan, Tianming Liu, Yajun Yan

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Bioresource technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Engineering theACS synthetic biology · 2026
    Article
  3. 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

6 authors.

Xinyu GongSchool of Chemical, Materials and Biomedical Engineering, College of Engineering, the University of Georgia, College of Engineering, Athens, GA 30602, USA.
Jianli ZhangSchool of Chemical, Materials and Biomedical Engineering, College of Engineering, the University of Georgia, College of Engineering, Athens, GA 30602, USA.
Qi GanSchool of Chemical, Materials and Biomedical Engineering, College of Engineering, the University of Georgia, College of Engineering, Athens, GA 30602, USA.
Wei RuanSchool of Computing, The University of Georgia, Athens, GA 30602, USA.
Tianming LiuSchool of Computing, The University of Georgia, Athens, GA 30602, USA.
Yajun YanSchool of Chemical, Materials and Biomedical Engineering, College of Engineering, the University of Georgia, College of Engineering, Athens, GA 30602, USA. Electronic address: yajunyan@uga.edu.

Funding

Engineering Dynamic Control of Natural Product Biosynthesis in BacteriaR35GM128620 · NIGMS · UNIVERSITY OF GEORGIA · PI Yajun Yan · 2018 to 2026
$3.0M
NIGMS NIH HHS R35 GM128620
6 · The paper itself

Abstract

Microbial production is a sustainable and economical approach to producing value-added compounds by functional enzyme application, precise metabolism regulation, effective strain development, and optimal bioprocess control. However, practical microbial production faces multiple problems. Specifically, insufficient functional enzymes limit biosynthetic pathway construction, while inadequate metabolic regulatory tools and suboptimal bioprocess control constrain productivity. The incorporation of artificial intelligence (AI) technologies addresses these problems by providing predictions of unexplored biological knowledge and perspectives covering protein functions, structures, strain phenotypes, and fermentation conditions. This review highlights recent AI applications to microbial production focusing on AI-enabled protein mining, engineering, and upstream bioprocess development. Finally, we propose future directions of developing and applying AI technologies to better aid microbial production.

Indexed as

Artificial IntelligenceFermentationMetabolic EngineeringArtificial intelligenceBioprocess controlMicrobial productionProtein mining and engineering

Identifiers

PMID40930281
PMCPMC13263902

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.