ArticleiLIVER2024
WGCNA combined with machine learning to explore potential biomarkers and treatment strategies for acute liver failure, with experimental validation.
Article in iLIVER, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed.
- Transcriptomic Identification of Diagnostic Biomarkers for Alcohol-Associated Liver Cirrhosis: Integration of Population-Level Epidemiology with Multi-Cohort Transcriptomic Analysis.International journal of molecular sciences · 2026Article
- Unbiased clustering of acute-on-chronic liver failure patients using machine learning in a real-world ICU cohort.Nature communications · 2026Article
- Spatial transcriptomic landscape and cellular neighborhood heterogeneity in cervical cancer: integrative single-cell and spatial RNA sequencing analysis.Discover oncology · 2025Article
- Donor-specific digital twin for living donor liver transplant recovery.Biology methods & protocols · 2025Article
- Dangua Fang induces anti-glucolipid metabolism disorder effects similar to those of direct NFIL3 inhibition.Frontiers in microbiology · 2025Article
- A new insight: crosstalk between neutrophil extracellular traps and the gut-liver axis for nonalcoholic fatty liver disease.Frontiers in immunology · 2025Review
- Human platelet lysate: a potential therapeutic for intracerebral hemorrhage.Frontiers in neuroscience · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and aims: To identify biomarkers to predict acute liver failure and investigate the mechanisms and immune-related pathways linked to its onset and progression. Methods: We analyzed gene expression differences between patients with acute liver failure (ALF) and controls in the GSE14668 dataset. Clinically relevant modules and key ALF-associated genes were identified using weighted gene co-expression network analysis (WGCNA) in conjunction with differential gene expression (DEG) analysis. Enrichment analysis was carried out and protein-protein interaction networks were constructed to understand the functions and pathways. Six potential diagnostic biomarkers were identified using machine learning algorithms. Diagnostic performance was assessed via column charts and area under the curve calculations. Single-sample gene set enrichment analysis evaluated the relationship between known marker gene sets and potential biomarker expression. We also examined diagnostic biomarker mRNA levels in ALF models Result: We found 352 DEGs associated with ALF. WGCNA analysis and intersecting DEGs identified 191 significant ALF-related genes. Machine learning identified Conclusion: We identified
Indexed as
Identifiers
What Socratic holds
Registered trials
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.