Evidence map›Paper›PMID 36454018›Full record

ArticleNucleic acids research2023

GeneFriends: gene co-expression databases and tools for humans and model organisms.

Priyanka Raina, Rodrigo Guinea, Kasit Chatsirisupachai, Inês Lopes, Zoya Farooq, Cristina Guinea, Csaba-Attila Solyom, João Pedro de Magalhães

Abstract read
In one paragraph

Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing 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

37 citing papers in PubMed.

  1. Article
  2. Review
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  7. Review
  8. SALVE: prediction of interorgan communication with transcriptome latent space representation.American journal of physiology. Heart and circulatory physiology · 2025
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  9. Article
  10. Transcriptomic Analysis ofBrain sciences · 2025
    Article
  11. Article
  12. Article
  13. Developing a novel medulloblastoma diagnostic with miRNA biomarkers and machine learning.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2025
    Article
  14. Article
  15. Article
  16. miRNA-Based Diagnosis of Schizophrenia Using Machine Learning.International journal of molecular sciences · 2025
    Article
  17. Article
  18. Article
  19. 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.

Priyanka RainaIntegrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK.
Rodrigo GuineaIntegrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK.
Kasit ChatsirisupachaiIntegrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK.
Inês LopesIntegrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK.
Zoya FarooqIntegrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK.
Cristina GuineaUCAL - Universidad de Ciencias y Artes de América Latina, Faculty of Design, Lima 15026, Perú.
Csaba-Attila SolyomIntegrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK.
João Pedro de MagalhãesIntegrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK.ORCID 0000-0002-6363-2465

Funding

Biotechnology and Biological Sciences Research CouncilWellcome Trust
6 · The paper itself

Abstract

Gene co-expression analysis has emerged as a powerful method to provide insights into gene function and regulation. The rapid growth of publicly available RNA-sequencing (RNA-seq) data has created opportunities for researchers to employ this abundant data to help decipher the complexity and biology of genomes. Co-expression networks have proven effective for inferring the relationship between the genes, for gene prioritization and for assigning function to poorly annotated genes based on their co-expressed partners. To facilitate such analyses we created previously an online co-expression tool for humans and mice entitled GeneFriends. To continue providing a valuable tool to the scientific community, we have now updated the GeneFriends database and website. Here, we present the new version of GeneFriends, which includes gene and transcript co-expression networks based on RNA-seq data from 46 475 human and 34 322 mouse samples. The new database also encompasses tissue-specific gene co-expression networks for 20 human and 21 mouse tissues, dataset-specific gene co-expression maps based on TCGA and GTEx projects and gene co-expression networks for additional seven model organisms (fruit fly, zebrafish, worm, rat, yeast, cow and chicken). GeneFriends is freely available at http://www.genefriends.org/.

Indexed as

Databases, GeneticGene Expression ProfilingGene Regulatory NetworksAnimalsHumansRNASequence Analysis, RNARNA

Identifiers

PMID36454018
PMCPMC9825523

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.