4 Works

A Reference Library for Characterizing Protein Subcellular Localizations by Image-Based Machine Learning

David Andrews
Libraries composed of 789,011 and 523,319 optically validated reference confocal micrographs of 17 and 20 EGFP fusion proteins localized at key cell organelles as landmarks in murine and human cells were generated for assignment of subcellular localization in mammalian cells. For each image of individual cells, 160 morphology and statistical features were used to train a random forests classifier to automatically assign the localization of proteins and dyes in both cell types and to analyze...

A Reference Library for Characterizing Protein Subcellular Localizations by Image-Based Machine Learning

David Andrews
Libraries composed of 789,011 and 523,319 optically validated reference confocal micrographs of 17 and 20 EGFP fusion proteins localized at key cell organelles as landmarks in murine and human cells were generated for assignment of subcellular localization in mammalian cells. For each image of individual cells, 160 morphology and statistical features were used to train a random forests classifier to automatically assign the localization of proteins and dyes in both cell types and to analyze...

Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability.

Brenda Andrews
Our ability to understand the genotype-to-phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single-cell level. To systematically assess cell-to-cell phenotypic variability, we combined automated yeast genetics, high-content screening and neural network-based image analysis of single cells, focussing on genes that influence the architecture of four subcellular compartments of the endocytic pathway as a model system. Our unbiased assessment of the morphology of these compartments — endocytic patch, actin patch,...

Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability.

Brenda Andrews
Our ability to understand the genotype-to-phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single-cell level. To systematically assess cell-to-cell phenotypic variability, we combined automated yeast genetics, high-content screening and neural network-based image analysis of single cells, focussing on genes that influence the architecture of four subcellular compartments of the endocytic pathway as a model system. Our unbiased assessment of the morphology of these compartments — endocytic patch, actin patch,...

Registration Year

  • 2021
    2
  • 2020
    2

Resource Types

  • Image
    4

Affiliations

  • University of Toronto
    4
  • Sunnybrook Hospital
    2
  • RIKEN
    2
  • Institute for Research in Biomedicine
    2
  • Memorial Sloan Kettering Cancer Center
    2
  • Institute of Biomedical Research of Barcelona
    2
  • Institució Catalana de Recerca i Estudis Avançats
    2