Evidence map›Paper›PMID 40397295›Full record

ReviewApplied biochemistry and biotechnology2025

Machine Learning Empowering Microbial Cell Factory: A Comprehensive Review.

Dechun Kong, Jinyi Qian, Cong Gao, Yuetong Wang, Tianqiong Shi, Chao Ye

Abstract readReview
PubMed Publisher
In one paragraph

Review in Applied biochemistry and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. 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.

Dechun KongSchool of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, People's Republic of China.
Jinyi QianMinistry of Education Key Laboratory of NSLSCS, Nanjing Normal University, Nanjing, 210023, People's Republic of China.
Cong GaoSchool of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi, 214122, People's Republic of China.
Yuetong WangSchool of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, People's Republic of China. wangyt@nnu.edu.cn.
Tianqiong ShiSchool of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, People's Republic of China. tqshi@njnu.edu.cn.
Chao YeSchool of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, People's Republic of China. chaoye09@njnu.edu.cn.

Funding

National Key R & D Program of China 2020YFA0908300National Natural Science Foundation of China No. 22208167National Natural Science Foundation of China No.22308167Natural Science Foundation of Jiangsu Province No. BK20230381the Jiangsu Basic Research Center for Synthetic Biology Grant BK20233003
6 · The paper itself

Abstract

The wide application of machine learning has provided more possibilities for biological manufacturing, and the combination of machine learning and synthetic biology technology has ignited even more brilliant sparks, which has created an unpredictable value for the upgrading of microbial cell factories. The review delves into the synergies between machine learning and synthetic biology to create research worth investigating in biotechnology. We explore relevant databases, toolboxes, and machine learning-derived models. Furthermore, we examine specific applications of this combined approach in chemical production, human health, and environmental remediation. By elucidating these successful integrations, this review aims to provide valuable guidance for future research at the intersection of biomanufacturing and artificial intelligence.

Indexed as

BiotechnologyMachine LearningSynthetic BiologyHumansBiomanufacturingDatabaseMachine learningMicrobial cell factorySynthetic biologyToolbox

Identifiers

PMID40397295

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.