Evidence map›Paper›PMID 36745614›Full record

ArticlePloS one2023

Gene expression prediction based on neighbour connection neural network utilizing gene interaction graphs.

Xuanyu Li, Xuan Zhang, Wenduo He, Deliang Bu, Sanguo Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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.

Xuanyu LiSchool of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China.
Xuan ZhangInstitute for Network Sciences and Cyberspace (INSC), Tsinghua University, Beijing, China.ORCID 0000-0003-0694-6799
Wenduo HeInstitute for Network Sciences and Cyberspace (INSC), Tsinghua University, Beijing, China.
Deliang BuSchool of Statistics, Capital University of Economics and Business, Beijing, China.
Sanguo ZhangSchool of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Having observed that gene expressions have a correlation, the Library of Integrated Network-based Cell-Signature program selects 1000 landmark genes to predict the remaining gene expression value. Further works have improved the prediction result by using deep learning models. However, these models ignore the latent structure of genes, limiting the accuracy of the experimental results. We therefore propose a novel neural network named Neighbour Connection Neural Network(NCNN) to utilize the gene interaction graph information. Comparing to the popular GCN model, our model incorperates the graph information in a better manner. We validate our model under two different settings and show that our model promotes prediction accuracy comparing to the other models.

Indexed as

Epistasis, GeneticLibrariesGene ExpressionGene LibraryNeural Networks, Computer

Identifiers

PMID36745614
PMCPMC9901809

What OpenQuestion holds

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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.