ArticleFrontiers in immunology2021
Unsupervised Clustering Reveals Distinct Subtypes of Biliary Atresia Based on Immune Cell Types and Gene Expression.
Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Multi-Omics Advances in Major Depressive Disorder for Molecular Insights, Biomarker Discovery, and Therapeutic Development.Aging and disease · 2025Review
- Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study.Journal of medical Internet research · 2023Article
- Development of liver inflammatory injury in biliary atresia: from basic to clinical research.Pediatric surgery international · 2023Review
- Genetic alterations and molecular mechanisms underlying hereditary intrahepatic cholestasis.Frontiers in pharmacology · 2023Review
- Cellular senescence in the cholangiopathies: a driver of immunopathology and a novel therapeutic target.Seminars in immunopathology · 2022Review
- The emerging roles of machine learning in cardiovascular diseases: a narrative review.Annals of translational medicine · 2022Review
- Genetic Factors and Their Role in the Pathogenesis of Biliary Atresia.Frontiers in pediatrics · 2022Review
- Identifying and validating molecular subtypes of biliary atresia using multiple high-throughput data integration analysis.Frontiers in immunology · 2022Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Biliary atresia (BA) is a severe cholangiopathy of early infancy that destroys cholangiocytes, obstructs ductular pathways and if left untreated, culminates to liver cirrhosis. Mechanisms underlying the etiological heterogeneity remain elusive and few studies have attempted phenotyping BA. We applied machine learning to identify distinct subtypes of BA which correlate with the underlying pathogenesis. Methods: The BA microarray dataset GSE46995 was downloaded from the Gene Expression Omnibus (GEO) database. Unsupervised hierarchical cluster analysis was performed to identify BA subtypes. Then, functional enrichment analysis was applied and hub genes identified to explore molecular mechanisms associated with each subtype. An independent dataset GSE15235 was used for validation process. Results: Based on unsupervised cluster analysis, BA patients can be classified into three distinct subtypes: Autoimmune, Viral and Embryonic subtypes. Functional analysis of Subtype 1 correlated with Fc Gamma Receptor (FCGR) activation and hub gene Conclusions: The classification of BA into different subtypes improves our current understanding of the underlying pathogenesis of BA and provides new insights for future studies.
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Registered trials
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