Evidence map›Paper›PMID 36303772›Full record

ReviewFrontiers in bioinformatics2021

Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb.

Ulf W Liebal, Sebastian Köbbing, Linus Netze, Artur M Schweidtmann, Alexander Mitsos, Lars M Blank

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in bioinformatics, 2021. 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. Article
  2. Fine-Tuning Gene Expression in Bacteria by Synthetic Promoters.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  3. Article
  4. Biotechnology Data Analysis Training with Jupyter Notebooks.Journal of microbiology & biology education · 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.

Ulf W LiebaliAMB-Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany.
Sebastian KöbbingiAMB-Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany.
Linus NetzeAVT-Process Systems Engineering, RWTH Aachen University, Aachen, Germany.
Artur M SchweidtmannDepartment of Chemical Engineering, Delft University of Technology, Delft, Netherlands.
Alexander MitsosAVT-Process Systems Engineering, RWTH Aachen University, Aachen, Germany.
Lars M BlankiAMB-Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic engineering relies on modifying gene expression to regulate protein concentrations and reaction activities. The gene expression is controlled by the promoter sequence, and sequence libraries are used to scan expression activities and to identify correlations between sequence and activity. We introduce a computational workflow called

Indexed as

biotechnologygene expressionjupyter notebookmachine learningstrain engineeringsynthetic biology

Identifiers

PMID36303772
PMCPMC9581000

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.