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 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.

Effects of latanoprost in iris bioidentification.

PURPOSE: Bioidentification is becoming increasingly important in everyday life. One of the most widespread methods of bioidentification is based on the structure of the iris. Iris photography has several advantages as an identification method: it is relatively simple and effective; it is non-invasive, and it is comparatively inexpensive. However, some medical conditions may change the appearance of the iris. This paper discusses the effects of latanoprost-induced pigmentation changes in iris bioidentification. METHODS: The study is based on four extreme cases of latanoprost-induced pigmentation changes. Iris photographs in these patients during treatment are compared with pretreatment photographs. The comparison is carried out with iris recognition software developed by our research group based on the principles of Daugman's well-known IrisCode. The system was evaluated with 595 iris comparisons. RESULTS: Iris photographs showing latanoprost-induced pigmentation changes were correctly matched with pretreatment photographs of the same irises with an error probability similar to that for matching equivalent pairs of photographs in intact eyes. CONCLUSIONS: Our results indicate that the pigmentation changes studied do not seem to have a significant effect on the standard identification algorithm.