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References "Большая вегетация летала по Украине" (in Russian). External links Category:2008 establishments in France Category:Bi-monthly magazines Category:Communist magazines Category:Free magazines Category:French-language magazines Category:Magazines established in 2008 Category:Magazines published in ParisThe Natural History of the Zoonotic Parasite Giardia and Its Clinical Implications: Coccidia of the Genus Giardia. Giardiasis is a protozoan infection caused by the flagellated parasite Giardia duodenalis. It is one of the most common parasitic infections of the human gastrointestinal tract and may cause chronic diarrheal illness in immunocompromised individuals. The etiologic role of G. duodenalis in the pathogenesis of giardiasis is not entirely clear, and its life cycle and pathogenesis still remain a matter of great interest. of SVM analysis results on the test dataset. The best performances were obtained using the $k$-nearest neighbor classifier on both test datasets with accuracies of $0.65$ and $0.63$, respectively. This suggests that the proposed approach is very efficient at discriminating between different tumor types. In addition, to allow for a direct comparison of the results of the proposed approach with other reported work, we generated two prediction maps for a number of tumor types. The resulting maps were compared with those obtained using a random forest-based approach. Both approaches were applied to the same set of tumor microarray images. The proposed approach yielded a higher predictive accuracy (0.91 vs. 0.90) as expected, as the random forest is able to identify complex classifiers when the number of cases is small and the individual classifier is not very good. Conclusion ========== In this work, we proposed a novel approach to classify tumor types based on the 3D morphology of tumor cells. The approach makes use of a collection of classification methods that were selected according to their performance on the validation dataset. The selected classification methods were compared in a cross-validation procedure. The resulting SVM classification models achieved a predictive accuracy of $0.91$ and $0.93$ on two different test datasets. 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