ArticleScientific reports2025
Machine learning enhanced immunologic risk assessments for solid organ transplantation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Predicting delayed graft function after kidney transplant: Do complex models help compared to standard statistics?World journal of nephrology · 2026Article
- The digital keystone: how artificial intelligence is reshaping HLA research and clinical practice.Immunogenetics · 2026Review
- From thresholds to trajectories: a perspective on reframing alloimmune risk for computational modeling in solid organ transplantation.Frontiers in immunology · 2026Review
- Multimodal artificial intelligence technology in the precision diagnosis and treatment of gastroenterology and hepatology: Innovative applications and challenges.World journal of gastroenterology · 2025Review
- From mitochondria to immune networks: new mesenchymal stem cell strategies to treat periodontitis.Stem cell research & therapy · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study was to enhance the prediction of solid-organ recipient and donor crossmatch compatibility by applying machine learning (ML). Prediction of crossmatch compatibility is complex and requires an understanding of the recipient and donor human leukocyte antigen (HLA) alleles and recipient HLA antibodies. An HLA allele imputation system that converts HLA antigens to alleles was developed to enhance the prediction's performance. The imputed and known HLA alleles were combined for recipient and donor with a recipient's HLA antibody profile. After processing, donor-specific antibodies were input into various ML models. Next, an ML model was developed and characterized based on determining donor-specific antibodies using the full HLA antibody profile of the recipient without laboratory interpretation. The models achieved an ROC-AUC of 0.975. These results demonstrate that the models can predict crossmatch reactivity and yield insight into the importance of specific HLA antibodies in the transplant-matching process. These data represent our understanding of personalized histocompatibility risk assessments.
Indexed as
Identifiers
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.