Leukemia prediction using sparse logistic regression.
Leukemia prediction using sparse logistic regression.
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DOI：
10.1371/journal.pone.0072932
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Nykter M
中科院分区：
文献类型：
--
作者：
Manninen T;Huttunen H;Ruusuvuori P;Nykter M
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 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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影响因子：
3.7
作者：
MASSEY, FJ
通讯作者：
MASSEY, FJ
影响因子：
48
作者：
Aghaeepour N;Finak G;FlowCAP Consortium;DREAM Consortium;Hoos H;Mosmann TR;Brinkman R;Gottardo R;Scheuermann RH
通讯作者：
Scheuermann RH
影响因子：
7.5
作者：
HOLTE, RC
通讯作者：
HOLTE, RC
影响因子：
--
作者：
KULLBACK, S;LEIBLER, RA
通讯作者：
LEIBLER, RA
DOI：
10.1111/j.1467-9868.2007.00627.x
发表时间：
2008-01-01
影响因子：
5.8
作者：
Meier, Lukas;van de Geer, Sara A.;Buhlmann, Peter
通讯作者：
Buhlmann, Peter