Evidence map›Paper›PMID 29686404›Full record

ReviewNature reviews. Gastroenterology & hepatology2018

Systems biology in hepatology: approaches and applications.

Adil Mardinoglu, Jan Boren, Ulf Smith, Mathias Uhlen, Jens Nielsen

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Gastroenterology & hepatology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed
5.8field-weighted citation impact, top 3% 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

69 citing papers in PubMed, 126 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Trial
  4. Combined Metabolic Activators Accelerates Recovery in Mild-to-Moderate COVID-19.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021
    Trial
  5. Review
  6. Article
  7. Article
  8. Review
  9. Longitudinal big biological data in the AI era.Molecular systems biology · 2025
    Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Genome-scale models in human metabologenomics.Nature reviews. Genetics · 2025
    Review
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Metabolic Modeling ofMetabolites · 2023
    Article

9 more citing papers are in PubMed but not listed here.

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 at 2 institutions in 1 country.

Adil MardinogluScience for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden. adilm@scilifelab.se.ORCID 0000-0002-4254-6090
Jan BorenDepartment of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden.ORCID 0000-0003-0786-8091
Ulf SmithDepartment of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden.ORCID 0000-0002-1439-4608
Mathias UhlenScience for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.ORCID 0000-0002-4858-8056
Jens NielsenScience for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.
Science for Life Laboratory · SESahlgrenska University Hospital · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detailed insights into the biological functions of the liver and an understanding of its crosstalk with other human tissues and the gut microbiota can be used to develop novel strategies for the prevention and treatment of liver-associated diseases, including fatty liver disease, cirrhosis, hepatocellular carcinoma and type 2 diabetes mellitus. Biological network models, including metabolic, transcriptional regulatory, protein-protein interaction, signalling and co-expression networks, can provide a scaffold for studying the biological pathways operating in the liver in connection with disease development in a systematic manner. Here, we review studies in which biological network models were used to integrate multiomics data to advance our understanding of the pathophysiological responses of complex liver diseases. We also discuss how this mechanistic approach can contribute to the discovery of potential biomarkers and novel drug targets, which might lead to the design of targeted and improved treatment strategies. Finally, we present a roadmap for the successful integration of models of the liver and other human tissues with the gut microbiota to simulate whole-body metabolic functions in health and disease.

Indexed as

AnimalsGastroenterologyHumansLiverLiver DiseasesSystems Biology

Identifiers

PMID29686404
OpenAlexW2801509790

What OpenQuestion holds

Textmetadata
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.