Evidence map›Paper›PMID 38099247›Full record

ArticleAdvanced genetics (Hoboken, N.J.)2023

Deep Learning-Assisted Design of Novel Promoters in

Xinglong Wang, Kangjie Xu, Yameng Tan, Shangyang Yu, Xinyi Zhao, Jingwen Zhou

Abstract read
In one paragraph

Article in Advanced genetics (Hoboken, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. UnconditionalSynthetic and systems biotechnology · 2026
    Article
  2. Article
  3. Review
  4. Review
  5. Article
  6. Deep Generative Model-Driven Design of Microbial Synthetic Promoters.Journal of microbiology and biotechnology · 2025
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Fine-Tuning Gene Expression in Bacteria by Synthetic Promoters.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  12. Deep Learning-Assisted Design of Novel Promoters inAdvanced genetics (Hoboken, N.J.) · 2023
    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

6 authors.

Xinglong WangEngineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology Jiangnan University 1800 Lihu Road Wuxi Jiangsu 214122 China.
Kangjie XuEngineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology Jiangnan University 1800 Lihu Road Wuxi Jiangsu 214122 China.
Yameng TanEngineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology Jiangnan University 1800 Lihu Road Wuxi Jiangsu 214122 China.
Shangyang YuEngineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology Jiangnan University 1800 Lihu Road Wuxi Jiangsu 214122 China.
Xinyi ZhaoEngineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology Jiangnan University 1800 Lihu Road Wuxi Jiangsu 214122 China.
Jingwen ZhouEngineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology Jiangnan University 1800 Lihu Road Wuxi Jiangsu 214122 China.ORCID https://orcid.org/0000-0002-3949-3733

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep learning (DL) approaches have the ability to accurately recognize promoter regions and predict their strength. Here, the potential for controllably designing active

Indexed as

deep learningdiffusion modelspromoter designpromoter recognitionpromoter strength

Identifiers

PMID38099247
PMCPMC10716054

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.