Unsupervised modeling of cell morphology dynamics for time-lapse microscopy.
Nat Methods
; 9(7): 711-3, 2012 May 27.
Article
em En
| MEDLINE
| ID: mdl-22635062
ABSTRACT
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Reconhecimento Automatizado de Padrão
/
Divisão Celular
/
Imagem com Lapso de Tempo
/
Microscopia de Fluorescência
/
Modelos Biológicos
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2012
Tipo de documento:
Article