This paper aims to predict the diagnosis of Acute Myeloid Leukemia (AML) from flow cytometry data using more automated decision making systems. This dataset was obtained from the DREAM6 AML Prediction Challenge. As the dataset is very large (84 dimensions), further processing using Linear Discriminant Analysis had to be conducted to reduce the dimensionality for simpler analysis.
Finally, a sparse logistic regression model will be used to classify a patient as being AML-positive or -negative with estimated probabilities.
Manninen T, Huttunen H, Ruusuvuori P, Nykter M (2013) Leukemia Prediction Using Sparse Logistic Regression. PLoS ONE 8(8): e72932. doi:10.1371/journal.pone.0072932
Team Trump [EBAC 04]