Evidence map›Paper›PMID 35464247›Full record

ArticleFood science and biotechnology2022

In silico prediction models for thyroid peroxidase inhibitors and their application to synthetic flavors.

Mihyun Seo, Changwon Lim, Hoonjeong Kwon

Open access · hybridAbstract read
In one paragraph

Article in Food science and biotechnology, 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
0.2field-weighted citation impact, top 54% of its field
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, 2 citations in OpenAlex.

  1. Review
  2. 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

3 authors at 2 institutions in 1 country.

Mihyun SeoDepartment of Food and Nutrition, Seoul National University, Seoul, Republic of Korea.
Changwon LimDepartment of Applied Statistics, Chung-Ang University, Seoul, Republic of Korea.
Hoonjeong KwonDepartment of Food and Nutrition, Seoul National University, Seoul, Republic of Korea.ORCID 0000-0001-8689-898X
Seoul National University · KRChung-Ang University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Systematic toxicity tests are often waived for the synthetic flavors as they are added in a very small amount in foods. However, their safety for some endpoints such as endocrine disruption should be concerned as they are likely to be active in low levels. In this case, structure-activity-relationship (SAR) models are good alternatives. In this study, therefore, binary, ternary, and quaternary prediction models were designed using simple or complex machine-learning methods. Overall, hard-voting classifiers outperformed other methods. The test scores for the best binary, ternary, and quaternary models were 0.6635, 0.5083, and 0.5217, respectively. Along with model development, some substructures including primary aromatic amine, (enol)ether, phenol, heterocyclic sulfur, and heterocyclic nitrogen, dominantly occurred in the most highly active compounds. The best predicting models were applied to synthetic flavors, and 22 agents appeared to have a strong inhibitory potential towards TPO activities. Supplementary Information: The online version contains supplementary material available at 10.1007/s10068-022-01041-y.

Indexed as

Machine learningQuantitative structure–activity relationship (QSAR)Synthetic flavorThyroid peroxidase inhibitor (TPO)Toxicity prediction

Identifiers

PMID35464247
PMCPMC8994803
OpenAlexW4220810863

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.