ArticleFrontiers in immunology2021
An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation.
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 9 papers.
What it found
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- Publication Trends of Research on Immune Tolerance After Kidney Transplantation: A Bibliometric Analysis from 1976 to 2024.Journal of multidisciplinary healthcare · 2026Article
- Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions.Renal failure · 2025Review
- The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Multi-omics Approach in Kidney Transplant: Lessons Learned from COVID-19 Pandemic.Current transplantation reports · 2023Article
- Beyond Human Limits: Harnessing Artificial Intelligence to Optimize Immunosuppression in Kidney Transplantation.Journal of clinical medicine research · 2023Review
- A novel stratification framework based on anoikis-related genes for predicting the prognosis in patients with osteosarcoma.Frontiers in immunology · 2023Article
- Natural killer cells in liver transplantation: Can we harness the power of the immune checkpoint to promote tolerance?Clinical and translational science · 2022Review
- Properties of regulatory B cells regulating B cell targets.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2021Article
- A Machine Learning Analysis of Prognostic Genes Associated With Allograft Tolerance After Renal Transplantation.Cell transplantationArticle
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
Efforts at finding potential biomarkers of tolerance after kidney transplantation have been hindered by limited sample size, as well as the complicated mechanisms underlying tolerance and the potential risk of rejection after immunosuppressant withdrawal. In this work, three different publicly available genome-wide expression data sets of peripheral blood lymphocyte (PBL) from 63 tolerant patients were used to compare 14 different machine learning models for their ability to predict spontaneous kidney graft tolerance. We found that the Best Subset Selection (BSS) regression approach was the most powerful with a sensitivity of 91.7% and a specificity of 93.8% in the test group, and a specificity of 86.1% and a sensitivity of 80% in the validation group. A feature set with five genes (HLA-DOA, TCL1A, EBF1, CD79B, and PNOC) was identified using the BSS model. EBF1 downregulation was also an independent factor predictive of graft rejection and graft loss. An AUC value of 84.4% was achieved using the two-gene signature (EBF1 and HLA-DOA) as an input to our classifier. Overall, our systematic machine learning exploration suggests novel biological targets that might affect tolerance to renal allografts, and provides clinical insights that can potentially guide patient selection for immunosuppressant withdrawal.
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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.