Evidence map›Paper›PMID 32656193›Full record

ArticleFrontiers in bioengineering and biotechnology2020

Detection and Independent Validation of Model-Based Quantitative Transcriptional Regulation Relationships Altered in Lung Cancers.

Meiyu Duan, Haoqiu Song, Chaoyu Wang, Jiaxin Zheng, Hui Xie, Yupeng He, Lan Huang, Fengfeng Zhou

Open access · goldAbstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
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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 at 2 institutions in 1 country.

Meiyu DuanKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
Haoqiu SongKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
Chaoyu WangKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Software, Jilin University, Changchun, China.
Jiaxin ZhengKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Software, Jilin University, Changchun, China.
Hui XieKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
Yupeng HeKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Software, Jilin University, Changchun, China.
Lan HuangKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
Fengfeng ZhouKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
Jilin University · CNHubei University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Differential expressions of genes are widely evaluated for the diagnosis and prognosis correlations with diseases. But limited studies investigate how transcriptional regulations are quantitatively altered in diseases. This study proposes a novel model-based quantitative measurement of transcriptional regulatory relationships between mRNA genes and Transcription Factor (TF) genes (mqTrans features). This study didn't consider the regulatory relationships between TF genes, so the mRNA genes were the protein-coding genes excluding the TF genes. The models are trained in the control samples in a lung cancer dataset and evaluated in two independent datasets and the hold-out testing samples from the third dataset. Twenty-nine mRNA genes are detected with transcriptional regulations quantitatively altered in lung cancers. The transcriptional modification technologies like RNA interference (RNAi) may be utilized to restore the altered transcriptional regulations in lung cancers.

Indexed as

mqTransmRNAquantitative measurementregressiontranscription factor

Identifiers

PMID32656193
PMCPMC7325891
OpenAlexW3034838301

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.