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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...

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Autores principales: Ceballos, Gerardo A., Hernandez, Luis F., Paredes, Daniel, Betancourt, Luis R., Abdulreda, Midhat H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
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.
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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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