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An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation
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 avail...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8297499/ https://www.ncbi.nlm.nih.gov/pubmed/34305931 http://dx.doi.org/10.3389/fimmu.2021.695806 |
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author | Fu, Qiang Agarwal, Divyansh Deng, Kevin Matheson, Rudy Yang, Hongji Wei, Liang Ran, Qing Deng, Shaoping Markmann, James F. |
author_facet | Fu, Qiang Agarwal, Divyansh Deng, Kevin Matheson, Rudy Yang, Hongji Wei, Liang Ran, Qing Deng, Shaoping Markmann, James F. |
author_sort | Fu, Qiang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8297499 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82974992021-07-23 An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation Fu, Qiang Agarwal, Divyansh Deng, Kevin Matheson, Rudy Yang, Hongji Wei, Liang Ran, Qing Deng, Shaoping Markmann, James F. Front Immunol Immunology 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. Frontiers Media S.A. 2021-07-08 /pmc/articles/PMC8297499/ /pubmed/34305931 http://dx.doi.org/10.3389/fimmu.2021.695806 Text en Copyright © 2021 Fu, Agarwal, Deng, Matheson, Yang, Wei, Ran, Deng and Markmann https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Fu, Qiang Agarwal, Divyansh Deng, Kevin Matheson, Rudy Yang, Hongji Wei, Liang Ran, Qing Deng, Shaoping Markmann, James F. An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation |
title | An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation |
title_full | An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation |
title_fullStr | An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation |
title_full_unstemmed | An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation |
title_short | An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation |
title_sort | unbiased machine learning exploration reveals gene sets predictive of allograft tolerance after kidney transplantation |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8297499/ https://www.ncbi.nlm.nih.gov/pubmed/34305931 http://dx.doi.org/10.3389/fimmu.2021.695806 |
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