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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Tapio Manninen, Heikki Huttunen, Pekka Ruusuvuori, Matti Nykter
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6251fdfee2b142e1b3b52154267ee548
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6251fdfee2b142e1b3b52154267ee548
record_format	dspace
spelling	oai:doaj.org-article:6251fdfee2b142e1b3b52154267ee5482021-11-18T08:57:35ZLeukemia prediction using sparse logistic regression.1932-620310.1371/journal.pone.0072932https://doaj.org/article/6251fdfee2b142e1b3b52154267ee5482013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24023658/?tool=EBIhttps://doaj.org/toc/1932-6203We 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 [Formula: 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.Tapio ManninenHeikki HuttunenPekka RuusuvuoriMatti NykterPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 8, p e72932 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tapio Manninen Heikki Huttunen Pekka Ruusuvuori Matti Nykter Leukemia prediction using sparse logistic regression.
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 [Formula: 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	article
author	Tapio Manninen Heikki Huttunen Pekka Ruusuvuori Matti Nykter
author_facet	Tapio Manninen Heikki Huttunen Pekka Ruusuvuori Matti Nykter
author_sort	Tapio Manninen
title	Leukemia prediction using sparse logistic regression.
title_short	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_sort	leukemia prediction using sparse logistic regression.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/6251fdfee2b142e1b3b52154267ee548
work_keys_str_mv	AT tapiomanninen leukemiapredictionusingsparselogisticregression AT heikkihuttunen leukemiapredictionusingsparselogisticregression AT pekkaruusuvuori leukemiapredictionusingsparselogisticregression AT mattinykter leukemiapredictionusingsparselogisticregression
_version_	1718421085163094016

Leukemia prediction using sparse logistic regression.

Ejemplares similares