Evidence map›Paper›PMID 36124777›Full record

ArticleBriefings in bioinformatics2022

CORN-Condition Orientated Regulatory Networks: bridging conditions to gene networks.

Ricky Wai Tak Leung, Xiaosen Jiang, Xueqing Zong, Yanhong Zhang, Xinlin Hu, Yaohua Hu, Jing Qin

Open access · hybridAbstract read
In one paragraph

Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 38% 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

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. CORN 2.0 - Condition Orientated Regulatory Networks 2.0.Computational and structural biotechnology journal · 2025
    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

7 authors at 4 institutions in 2 countries.

Ricky Wai Tak LeungSchool of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China.
Xiaosen JiangCollege of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
Xueqing ZongSchool of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China.
Yanhong ZhangSchool of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China.
Xinlin HuCollege of Mathematics and Statistics, Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China.
Yaohua HuCollege of Mathematics and Statistics, Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China.
Jing QinSchool of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China.ORCID 0000-0002-8327-8846
Sun Yat-sen University · CNHong Kong Polytechnic University · HKShenzhen University · CNUniversity of Chinese Academy of Sciences · CN

Funding

National Natural Science Foundation of China 12071306Natural Science Foundation of Guangdong Province of China 2019A1515011917Natural Science Foundation of Shenzhen JCYJ20190808173603590Project of Educational Commission of Guangdong Province of China 2021KTSCX103
6 · The paper itself

Abstract

A transcriptional regulatory network (TRN) is a collection of transcription regulators with their associated downstream genes, which is highly condition-specific. Understanding how cell states can be programmed through small molecules/drugs or conditions by modulating the whole gene expression system granted us the potential to amend abnormal cells and cure diseases. Condition Orientated Regulatory Networks (CORN, https://qinlab.sysu.edu.cn/home) is a library of condition (small molecule/drug treatments and gene knockdowns)-based transcriptional regulatory sub-networks (TRSNs) that come with an online TRSN matching tool. It allows users to browse condition-associated TRSNs or match those TRSNs by inputting transcriptomic changes of interest. CORN utilizes transcriptomic changes data after specific conditional treatment in cells, and in vivo transcription factor (TF) binding data in cells, by combining TF binding information and calculations of significant expression alterations of TFs and genes after the conditional treatments, TRNs under the effect of different conditions were constructed. In short, CORN associated 1805 different types of specific conditions (small molecule/drug treatments and gene knockdowns) to 9553 TRSNs in 25 human cell lines, involving 204TFs. By linking and curating specific conditions to responsive TRNs, the scientific community can now perceive how TRNs are altered and controlled by conditions alone in an organized manner for the first time. This study demonstrated with examples that CORN can aid the understanding of molecular pathology, pharmacology and drug repositioning, and screened drugs with high potential for cancer and coronavirus disease 2019 (COVID-19) treatments.

Indexed as

COVID-19Gene Regulatory NetworksHumansTranscription FactorsTranscriptomeTranscription Factorsconditionsdrugsregulatory networkssmall moleculestranscriptional control

Identifiers

PMID36124777
PMCPMC9677472
OpenAlexW4296483470

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.