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A machine learning approach to predict pancreatic islet grafts rejection versus tolerance
The application of artificial intelligence (AI) and machine learning (ML) in biomedical research promises to unlock new information from the vast amounts of data being generated through the delivery of healthcare and the expanding high-throughput research applications. Such information can aid medic...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7644021/ https://www.ncbi.nlm.nih.gov/pubmed/33152016 http://dx.doi.org/10.1371/journal.pone.0241925 |
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author | Ceballos, Gerardo A. Hernandez, Luis F. Paredes, Daniel Betancourt, Luis R. Abdulreda, Midhat H. |
author_facet | Ceballos, Gerardo A. Hernandez, Luis F. Paredes, Daniel Betancourt, Luis R. Abdulreda, Midhat H. |
author_sort | Ceballos, Gerardo A. |
collection | PubMed |
description | The application of artificial intelligence (AI) and machine learning (ML) in biomedical research promises to unlock new information from the vast amounts of data being generated through the delivery of healthcare and the expanding high-throughput research applications. Such information can aid medical diagnoses and reveal various unique patterns of biochemical and immune features that can serve as early disease biomarkers. In this report, we demonstrate the feasibility of using an AI/ML approach in a relatively small dataset to discriminate among three categories of samples obtained from mice that either rejected or tolerated their pancreatic islet allografts following transplant in the anterior chamber of the eye, and from naïve controls. We created a locked software based on a support vector machine (SVM) technique for pattern recognition in electropherograms (EPGs) generated by micellar electrokinetic chromatography and laser induced fluorescence detection (MEKC-LIFD). Predictions were made based only on the aligned EPGs obtained in microliter-size aqueous humor samples representative of the immediate local microenvironment of the islet allografts. The analysis identified discriminative peaks in the EPGs of the three sample categories. Our classifier software was tested with targeted and untargeted peaks. Working with the patterns of untargeted peaks (i.e., based on the whole pattern of EPGs), it was able to achieve a 21 out of 22 positive classification score with a corresponding 95.45% prediction accuracy among the three sample categories, and 100% accuracy between the rejecting and tolerant recipients. These findings demonstrate the feasibility of AI/ML approaches to classify small numbers of samples and they warrant further studies to identify the analytes/biochemicals corresponding to discriminative features as potential biomarkers of islet allograft immune rejection and tolerance. |
format | Online Article Text |
id | pubmed-7644021 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-76440212020-11-16 A machine learning approach to predict pancreatic islet grafts rejection versus tolerance Ceballos, Gerardo A. Hernandez, Luis F. Paredes, Daniel Betancourt, Luis R. Abdulreda, Midhat H. PLoS One Research Article The application of artificial intelligence (AI) and machine learning (ML) in biomedical research promises to unlock new information from the vast amounts of data being generated through the delivery of healthcare and the expanding high-throughput research applications. Such information can aid medical diagnoses and reveal various unique patterns of biochemical and immune features that can serve as early disease biomarkers. In this report, we demonstrate the feasibility of using an AI/ML approach in a relatively small dataset to discriminate among three categories of samples obtained from mice that either rejected or tolerated their pancreatic islet allografts following transplant in the anterior chamber of the eye, and from naïve controls. We created a locked software based on a support vector machine (SVM) technique for pattern recognition in electropherograms (EPGs) generated by micellar electrokinetic chromatography and laser induced fluorescence detection (MEKC-LIFD). Predictions were made based only on the aligned EPGs obtained in microliter-size aqueous humor samples representative of the immediate local microenvironment of the islet allografts. The analysis identified discriminative peaks in the EPGs of the three sample categories. Our classifier software was tested with targeted and untargeted peaks. Working with the patterns of untargeted peaks (i.e., based on the whole pattern of EPGs), it was able to achieve a 21 out of 22 positive classification score with a corresponding 95.45% prediction accuracy among the three sample categories, and 100% accuracy between the rejecting and tolerant recipients. These findings demonstrate the feasibility of AI/ML approaches to classify small numbers of samples and they warrant further studies to identify the analytes/biochemicals corresponding to discriminative features as potential biomarkers of islet allograft immune rejection and tolerance. Public Library of Science 2020-11-05 /pmc/articles/PMC7644021/ /pubmed/33152016 http://dx.doi.org/10.1371/journal.pone.0241925 Text en © 2020 Ceballos et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Ceballos, Gerardo A. Hernandez, Luis F. Paredes, Daniel Betancourt, Luis R. Abdulreda, Midhat H. A machine learning approach to predict pancreatic islet grafts rejection versus tolerance |
title | A machine learning approach to predict pancreatic islet grafts rejection versus tolerance |
title_full | A machine learning approach to predict pancreatic islet grafts rejection versus tolerance |
title_fullStr | A machine learning approach to predict pancreatic islet grafts rejection versus tolerance |
title_full_unstemmed | A machine learning approach to predict pancreatic islet grafts rejection versus tolerance |
title_short | A machine learning approach to predict pancreatic islet grafts rejection versus tolerance |
title_sort | machine learning approach to predict pancreatic islet grafts rejection versus tolerance |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7644021/ https://www.ncbi.nlm.nih.gov/pubmed/33152016 http://dx.doi.org/10.1371/journal.pone.0241925 |
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