Evidence map›Paper›PMID 40678148›Full record

ArticleSynthetic and systems biotechnology2025

Decoding yeast transcriptional regulation via a data-and mechanism-driven distributed large-scale network model.

Xingcun Fan, Guangming Xiang, Wenbin Liao, Luchi Xiao, Siwei He, Na Luo, Hongzhong Lu, Xuefeng Yan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Synthetic and systems biotechnology, 2025. 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. 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

8 authors.

Xingcun FanKey Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, 130 Meilong Rd, Shanghai, 200237, PR China.
Guangming XiangState Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Rd, Shanghai, 200240, PR China.
Wenbin LiaoKey Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, 130 Meilong Rd, Shanghai, 200237, PR China.
Luchi XiaoState Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Rd, Shanghai, 200240, PR China.
Siwei HeState Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Rd, Shanghai, 200240, PR China.
Na LuoKey Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, 130 Meilong Rd, Shanghai, 200237, PR China.
Hongzhong LuState Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Rd, Shanghai, 200240, PR China.
Xuefeng YanKey Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, 130 Meilong Rd, Shanghai, 200237, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The complex transcriptional regulatory relationships among genes influence gene expression levels and play a crucial role in determining cellular phenotypes. In this study, we propose a novel, distributed, large-scale transcriptional regulatory neural network model (DLTRNM), which integrates prior knowledge into the reconstruction of pre-trained machine learning models, followed by fine-tuning. Using

Indexed as

Distributed large-scale neural networkMechanistic and data-drivenS. cerevisiaeTransfer learningTRN

Identifiers

PMID40678148
PMCPMC12268561

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.