Ewa Szczurek

TL;DR: This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years in single-cell data science.

TL;DR: A de novo approach is taken to search for mutually exclusive patterns in cancer genomic data, where these alterations tend not to occur together in the same patient.

TL;DR: This work proposes an accurate, reliable and active (ARA) image classification framework and introduces a new Bayesian Convolutional Neural Network (ARA-CNN) for classifying histopathological images of colorectal cancer, which achieves exceptional classification accuracy, outperforming other models trained on the same dataset.

TL;DR: A fully probabilistic, generative model of mutual exclusivity is developed, explicitly taking coverage, impurity, as well as error rates into account, and several significant patterns that are biologically relevant are identified, most of which would not be detected by previous approaches.