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Plos One : Leukemia Prediction Using Sparse Logistic Regression, Volume 8

By Hills, Robert K.

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Book Id: WPLBN0003951321
Format Type: PDF eBook :
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Reproduction Date: 2015

Title: Plos One : Leukemia Prediction Using Sparse Logistic Regression, Volume 8  
Author: Hills, Robert K.
Volume: Volume 8
Language: English
Subject: Journals, Science, Medical Science
Collections: Periodicals: Journal and Magazine Collection (Contemporary)
Historic
Publication Date:
Publisher: Plos

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Hills, R. K. (n.d.). Plos One : Leukemia Prediction Using Sparse Logistic Regression, Volume 8. Retrieved from http://www.nationalpubliclibrary.info/


Description
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 ‘1 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.

 

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