Evidence map›Paper›PMID 40270708›Full record

ArticleComputational and structural biotechnology journal2025

CORN 2.0 - Condition Orientated Regulatory Networks 2.0.

Ricky Wai Tak Leung, Xinying Zhang, Zhuobin Chen, Yuyun Liang, Simei Huang, Zixin Yang, Xueqing Zong, Xiaosen Jiang, Runming Lin, Wenbin Deng and 2 more

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 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

12 authors.

Ricky Wai Tak LeungSchool of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
Xinying ZhangSchool of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
Zhuobin ChenSchool of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
Yuyun LiangSchool of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
Simei HuangSchool of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
Zixin YangSchool of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
Xueqing ZongSchool of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
Xiaosen JiangCollege of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
Runming LinBGI-Shenzhen, Shenzhen, Guangdong 518103, China.
Wenbin DengSchool of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
Yaohua HuSchool of Mathematical Sciences, Shenzhen University, Shenzhen, Guangdong 518060, China.
Jing QinSchool of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene regulation is a fundamental process that allows organisms to adapt to their environment and increase complexity through the action of nucleic acid-binding proteins (NBPs), such as transcription factors (TFs), which regulate specific sets of genes under distinct conditions. These regulatory interactions form transcriptional regulatory networks (TRNs), which can be further broken down into transcriptional regulatory sub-networks (TRSNs) centered around individual TFs. TRSNs are more stable and practical for analysis, making them ideal for studying gene regulation under specific conditions. Condition-Oriented Regulatory Networks (CORN, https://qinlab.sysu.edu.cn/corn/home) is a comprehensive library of condition-based TRSNs, including those induced by natural compounds, small molecules, drug treatments, and gene perturbations. CORN 2.0 represents a significant update, associating 7540 specific conditions with 71934 TRSNs across 52 human cell lines, involving 542 transcription factors (TFs). Notably, CORN 2.0 includes 1550 natural compound-triggered TRSNs, providing a valuable resource for studying the pharmacological effects of natural products. This study demonstrates the utility of CORN in three key areas: personalized medicine, induced pluripotency transitions, and natural compound-associated pharmacology. By linking specific conditions to their corresponding TRSNs, CORN enables researchers to explore how gene regulatory networks are altered under various conditions, offering insights into disease mechanisms and potential therapeutic interventions.

Indexed as

Cell fateChinese medicineEmbryonic stem cellsGene regulatory networkHerbal medicineInduced pluripotencyNatural compoundsNatural productsPersonalized medicineTumor heterogeneity

Identifiers

PMID40270708
PMCPMC12017979

What Socratic 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 Socratic graph.