ArticleCell reports. Medicine2022
A network-based approach to identify expression modules underlying rejection in pediatric liver transplantation.
Article in Cell reports. Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
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.
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Who cites it
11 citing papers in PubMed, 17 citations in OpenAlex.
- Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.Journal of clinical medicine · 2026Review
- Exploring Chronic Rejection in Organ Transplantation Through Computational Modeling.Results and problems in cell differentiation · 2026Review
- Single-cell transcriptional landscape of liver transplant rejection reveals tissue persistence of clonally expanded, treatment-resistant T cells.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2025Article
- Prediction of the risk of transplant rejection based on RNA sequencing data of PBMCs before transplantation.Scientific reports · 2025Article
- Diagnostic and predictive biomarkers of acute rejection after liver transplantation.International journal of surgery (London, England) · 2025Review
- Review
- Artificial Intelligence in Pediatric Liver Transplantation: Opportunities and Challenges of a New Era.Children (Basel, Switzerland) · 2024Review
- Defining the T cell transcriptional landscape in pediatric liver transplant rejection at single cell resolution.bioRxiv : the preprint server for biology · 2024Article
- From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches.Human vaccines & immunotherapeutics · 2023Review
- Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning.Cell reports. Medicine · 2022Review
- The Role of Dynamic DNA Methylation in Liver Transplant Rejection in Children.Transplantation direct · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Selecting the right immunosuppressant to ensure rejection-free outcomes poses unique challenges in pediatric liver transplant (LT) recipients. A molecular predictor can comprehensively address these challenges. Currently, there are no well-validated blood-based biomarkers for pediatric LT recipients before or after LT. Here, we discover and validate separate pre- and post-LT transcriptomic signatures of rejection. Using an integrative machine learning approach, we combine transcriptomics data with the reference high-quality human protein interactome to identify network module signatures, which underlie rejection. Unlike gene signatures, our approach is inherently multivariate and more robust to replication and captures the structure of the underlying network, encapsulating additive effects. We also identify, in an individual-specific manner, signatures that can be targeted by current anti-rejection drugs and other drugs that can be repurposed. Our approach can enable personalized adjustment of drug regimens for the dominant targetable pathways before and after LT in children.
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What OpenQuestion 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 OpenQuestion graph.