Cargando…
Automated analysis of high‐content microscopy data with deep learning
Existing computational pipelines for quantitative analysis of high‐content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the...
Autores principales: | Kraus, Oren Z, Grys, Ben T, Ba, Jimmy, Chong, Yolanda, Frey, Brendan J, Boone, Charles, Andrews, Brenda J |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408780/ https://www.ncbi.nlm.nih.gov/pubmed/28420678 http://dx.doi.org/10.15252/msb.20177551 |
Ejemplares similares
-
Classifying and segmenting microscopy images with deep multiple instance learning
por: Kraus, Oren Z., et al.
Publicado: (2016) -
Machine learning and computer vision approaches for phenotypic profiling
por: Grys, Ben T., et al.
Publicado: (2017) -
Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins
por: Lu, Alex X, et al.
Publicado: (2018) -
CYCLoPs: A Comprehensive Database Constructed from Automated Analysis of Protein Abundance and Subcellular Localization Patterns in Saccharomyces cerevisiae
por: Koh, Judice L. Y., et al.
Publicado: (2015) -
Automated microscopy for high-content RNAi screening
por: Conrad, Christian, et al.
Publicado: (2010)