no code implementations • 13 Mar 2013 • Fabio Parisi, Francesco Strino, Boaz Nadler, Yuval Kluger
This scenario is different from the standard supervised setting, where each classifier accuracy can be assessed using available labeled data, and raises two questions: given only the predictions of several classifiers over a large set of unlabeled test data, is it possible to a) reliably rank them; and b) construct a meta-classifier more accurate than most classifiers in the ensemble?
no code implementations • 9 Jan 2013 • Francesco Strino, Fabio Parisi, Mariann Micsinai, Yuval Kluger
Herein we propose a framework for deconvolving data from a single genome-wide experiment to infer the composition, abundance and evolutionary paths of the underlying cell subpopulations of a tumor.