Evidence map›Paper›PMID 30899072›Full record

ArticleScientific reports2019

Gene Saturation: An Approach to Assess Exploration Stage of Gene Interaction Networks.

Ziqiao Yin, Binghui Guo, Zhilong Mi, Jiahui Li, Zhiming Zheng

Abstract read
In one paragraph

Article in Scientific reports, 2019. 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

5 authors.

Ziqiao YinBeijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China.
Binghui GuoBeijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China. guobinghui@buaa.edu.cn.
Zhilong MiBeijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China.
Jiahui LiBeijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China.
Zhiming ZhengBeijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The gene interaction network is one of the most important biological networks and has been studied by many researchers. The gene interaction network provides information about whether the genes in the network can cause or heal diseases. As gene-gene interaction relations are constantly explored, gene interaction networks are evolving. To describe how much a gene has been studied, an approach based on a logistic model for each gene called gene saturation has been proposed, which in most cases, satisfies non-decreasing, correlation and robustness principles. The average saturation of a group of genes can be used to assess the network constructed by these genes. Saturation reflects the distance between known gene interaction networks and the real gene interaction network in a cell. Furthermore, the saturation values of 546 disease gene networks that belong to 15 categories of diseases have been calculated. The disease gene networks' saturation for cancer is significantly higher than that of all other diseases, which means that the disease gene networks' structure for cancer has been more deeply studied than other disease. Gene saturation provides guidance for selecting an experimental subject gene, which may have a large number of unknown interactions.

Indexed as

Computational BiologyAlgorithmsEpistasis, GeneticGene Expression ProfilingGene Regulatory NetworksHumans

Identifiers

PMID30899072
PMCPMC6428845

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.