Evidence map›Paper›PMID 35140556›Full record

ArticleEngineering in life sciences2022

Phenotype analysis of cultivation processes via unsupervised machine learning: Demonstration for

Yaeseong Hong, Tom Nguyen, Philipp Arbter, Tyll Utesch, An-Ping Zeng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Engineering in life sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. AI-nanotech synergies: recent advances in sustainable bio-manufacturing.World journal of microbiology & biotechnology · 2026
    Review
  2. 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

5 authors.

Yaeseong HongInstitute of Bioprocess and Biosystems Engineering Hamburg University of Technology TUHH Hamburg Germany.ORCID https://orcid.org/0000-0002-5607-3618
Tom NguyenInstitute of Bioprocess and Biosystems Engineering Hamburg University of Technology TUHH Hamburg Germany.
Philipp ArbterInstitute of Bioprocess and Biosystems Engineering Hamburg University of Technology TUHH Hamburg Germany.ORCID https://orcid.org/0000-0002-5166-0303
Tyll UteschInstitute of Bioprocess and Biosystems Engineering Hamburg University of Technology TUHH Hamburg Germany.
An-Ping ZengInstitute of Bioprocess and Biosystems Engineering Hamburg University of Technology TUHH Hamburg Germany.ORCID https://orcid.org/0000-0001-9768-7096

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A novel approach of phenotype analysis of fermentation-based bioprocesses based on unsupervised learning (clustering) is presented. As a prior identification of phenotypes and conditional interrelations is desired to control fermentation performance, an automated learning method to output reference phenotypes (defined as vector of biomass-specific rates) was developed and the necessary computing process and parameters were assessed. For its demonstration, time series data of 90

Indexed as

automated fermentation analysisClostridium pasteurianumphenotype analysisprocess monitoringunsupervised learning

Identifiers

PMID35140556
PMCPMC8811730

What OpenQuestion holds

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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.