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Leukemia Prediction Using Sparse Logistic Regression
We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML) from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in flow cytometry measurements from a single patie...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3758279/ https://www.ncbi.nlm.nih.gov/pubmed/24023658 http://dx.doi.org/10.1371/journal.pone.0072932 |
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author | Manninen, Tapio Huttunen, Heikki Ruusuvuori, Pekka Nykter, Matti |
author_facet | Manninen, Tapio Huttunen, Heikki Ruusuvuori, Pekka Nykter, Matti |
author_sort | Manninen, Tapio |
collection | PubMed |
description | We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML) from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in flow cytometry measurements from a single patient and gives a confidence score of the patient being AML-positive. Our solution is based on an [Image: see text] regularized logistic regression model that aggregates AML test statistics calculated from individual test tubes with different cell populations and fluorescent markers. The model construction is entirely data driven and no prior biological knowledge is used. The described solution scored a 100% classification accuracy in the DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukaemia Challenge against a golden standard consisting of 20 AML-positive and 160 healthy patients. Here we perform a more extensive validation of the prediction model performance and further improve and simplify our original method showing that statistically equal results can be obtained by using simple average marker intensities as features in the logistic regression model. In addition to the logistic regression based model, we also present other classification models and compare their performance quantitatively. The key benefit in our prediction method compared to other solutions with similar performance is that our model only uses a small fraction of the flow cytometry measurements making our solution highly economical. |
format | Online Article Text |
id | pubmed-3758279 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-37582792013-09-10 Leukemia Prediction Using Sparse Logistic Regression Manninen, Tapio Huttunen, Heikki Ruusuvuori, Pekka Nykter, Matti PLoS One Research Article We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML) from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in flow cytometry measurements from a single patient and gives a confidence score of the patient being AML-positive. Our solution is based on an [Image: see text] regularized logistic regression model that aggregates AML test statistics calculated from individual test tubes with different cell populations and fluorescent markers. The model construction is entirely data driven and no prior biological knowledge is used. The described solution scored a 100% classification accuracy in the DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukaemia Challenge against a golden standard consisting of 20 AML-positive and 160 healthy patients. Here we perform a more extensive validation of the prediction model performance and further improve and simplify our original method showing that statistically equal results can be obtained by using simple average marker intensities as features in the logistic regression model. In addition to the logistic regression based model, we also present other classification models and compare their performance quantitatively. The key benefit in our prediction method compared to other solutions with similar performance is that our model only uses a small fraction of the flow cytometry measurements making our solution highly economical. Public Library of Science 2013-08-30 /pmc/articles/PMC3758279/ /pubmed/24023658 http://dx.doi.org/10.1371/journal.pone.0072932 Text en © 2013 Manninen 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Manninen, Tapio Huttunen, Heikki Ruusuvuori, Pekka Nykter, Matti Leukemia Prediction Using Sparse Logistic Regression |
title | Leukemia Prediction Using Sparse Logistic Regression |
title_full | Leukemia Prediction Using Sparse Logistic Regression |
title_fullStr | Leukemia Prediction Using Sparse Logistic Regression |
title_full_unstemmed | Leukemia Prediction Using Sparse Logistic Regression |
title_short | Leukemia Prediction Using Sparse Logistic Regression |
title_sort | leukemia prediction using sparse logistic regression |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3758279/ https://www.ncbi.nlm.nih.gov/pubmed/24023658 http://dx.doi.org/10.1371/journal.pone.0072932 |
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