INVESTIGADORES
FEDEDA Juan Pablo
artículos
Título:
Unsupervised modeling of cell morphology dynamics for time-lapse microscopy
Autor/es:
QING ZHONG; ALBERTO GIOVANNI BUSETTO; JUAN P FEDEDA; JOACHIM M BUHMANN; DANIEL W GERLICH
Revista:
NATURE METHODS
Editorial:
NATURE PUBLISHING GROUP
Referencias:
Lugar: Londres; Año: 2012 vol. 9 p. 711 - 713
ISSN:
1548-7091
Resumen:
Analysis of cellular phenotypes in large imaging data sets conventionally involves supervised statistical methods, which require user-annotated training data. This paper introduces an unsupervised learning method, based on temporally constrained combinatorial clustering, for automatic prediction of cell morphology classes in time-resolved images. We applied the unsupervised method to diverse fluorescent markers and screening data and validated accurate classification of human cell phenotypes, demonstrating fully objective data labeling in image-based systems biology.
