Evidence map›Paper›PMID 42124427›Full record

ArticleJournal of microbiology and biotechnology2026

Prediction-Guided Biotransformation of

Chien-Yu Wu, Hsiou-Yu Ding, Huei-Ju Ting, Tzi-Yuan Wang, Tzu-Hsuan Li, Jou-Yi Chen, Hui-Ling Huang, Jiumn-Yih Wu, Te-Sheng Chang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of microbiology and biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Chien-Yu WuDepartment of Biological Sciences and Technology, National University of Tainan, Tainan 700301, Taiwan.
Hsiou-Yu DingDepartment of Cosmetic Science, Chia Nan University of Pharmacy and Science, Tainan 717301, Taiwan.
Huei-Ju TingDepartment of Biological Sciences and Technology, National University of Tainan, Tainan 700301, Taiwan.
Tzi-Yuan WangBiodiversity Research Center, Academia Sinica, Taipei 115201, Taiwan.
Tzu-Hsuan LiDepartment of Biological Sciences and Technology, National University of Tainan, Tainan 700301, Taiwan.
Jou-Yi ChenDepartment of Biological Sciences and Technology, National University of Tainan, Tainan 700301, Taiwan.
Hui-Ling HuangDepartment of Biological Sciences and Technology, National University of Tainan, Tainan 700301, Taiwan.
Jiumn-Yih WuDepartment of Food Science, National Quemoy University, Kinmen County 892009, Taiwan.
Te-Sheng ChangDepartment of Biological Sciences and Technology, National University of Tainan, Tainan 700301, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Discovering novel bioactive compounds is a fundamental goal in scientific research. To this end, our study employed a predicted data mining approach (PDMA) to efficiently screen for biotransformable precursors capable of yielding new bioactive derivatives. Using the PDMA strategy, we identified

Indexed as

Anti-Inflammatory AgentsAntineoplastic AgentsAntioxidantsAnimalsBacillus megateriumBiotransformationCyclooxygenase 2HydroxylationInterleukin-1betaInterleukin-6LipopolysaccharidesMacrophagesMiceMonophenol MonooxygenaseNitric Oxide Synthase Type IIRAW 264.7 CellsAnti-Inflammatory AgentsAntineoplastic AgentsAntioxidantsCyclooxygenase 2Interleukin-1betaInterleukin-6LipopolysaccharidesMonophenol MonooxygenaseNitric Oxide Synthase Type IITumor Necrosis Factor-alphaAnti-inflammationAnti-melanomaAntioxidantBiotransformationHydroxylationPredicted data mining approach

Identifiers

PMID42124427
PMCPMC13173354

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.