Cargando…
Environmental properties of cells improve machine learning-based phenotype recognition accuracy
To answer major questions of cell biology, it is often essential to understand the complex phenotypic composition of cellular systems precisely. Modern automated microscopes produce vast amounts of images routinely, making manual analysis nearly impossible. Due to their efficiency, machine learning-...
Autores principales: | Toth, Timea, Balassa, Tamas, Bara, Norbert, Kovacs, Ferenc, Kriston, Andras, Molnar, Csaba, Haracska, Lajos, Sukosd, Farkas, Horvath, Peter |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6031649/ https://www.ncbi.nlm.nih.gov/pubmed/29973621 http://dx.doi.org/10.1038/s41598-018-28482-y |
Ejemplares similares
-
Intelligent image-based in situ single-cell isolation
por: Brasko, Csilla, et al.
Publicado: (2018) -
The Combination of Single-Cell and Next-Generation Sequencing Can Reveal Mosaicism for BRCA2 Mutations and the Fine Molecular Details of Tumorigenesis
por: Gráf, Alexandra, et al.
Publicado: (2021) -
nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer
por: Hollandi, Reka, et al.
Publicado: (2020) -
Fisheye transformation enhances deep-learning-based single-cell phenotyping by including cellular microenvironment
por: Toth, Timea, et al.
Publicado: (2022) -
Simultaneous detection of BRCA mutations and large genomic rearrangements in germline DNA and FFPE tumor samples
por: Enyedi, Márton Zsolt, et al.
Publicado: (2016)