AIMC Topic: Organelles

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AI-powered transmitted light microscopy for functional analysis of live cells.

Scientific reports Dec 5, 2019
Transmitted light microscopy can readily visualize the morphology of living cells. Here, we introduce artificial-intelligence-powered transmitted light microscopy (AIM) for subcellular structure identification and labeling-free functional analysis of...
Primary Cell Culture Organelles Humans Intravital Microscopy Animals Dendritic Cells Mice Neural Networks, Computer Microscopy, Fluorescence Female Cell Line, Tumor
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Incorporating organelle correlations into semi-supervised learning for protein subcellular localization prediction.

Bioinformatics (Oxford, England) Apr 23, 2016
MOTIVATION: Bioimages of subcellular protein distribution as a new data source have attracted much attention in the field of automated prediction of proteins subcellular localization. Performance of existing systems is significantly limited by the sm...
Organelles Bayes Theorem Fluorescent Antibody Technique Algorithms Computational Biology Supervised Machine Learning Immunohistochemistry Humans Protein Transport
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Improving and evaluating deep learning models of cellular organization.

Bioinformatics (Oxford, England) Nov 30, 2022
MOTIVATION: Cells contain dozens of major organelles and thousands of other structures, many of which vary extensively in their number, size, shape and spatial distribution. This complexity and variation dramatically complicates the use of both tradi...
Image Processing, Computer-Assisted Organelles Deep Learning
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DeepLearnMOR: a deep-learning framework for fluorescence image-based classification of organelle morphology.

Plant physiology Aug 3, 2021
The proper biogenesis, morphogenesis, and dynamics of subcellular organelles are essential to their metabolic functions. Conventional techniques for identifying, classifying, and quantifying abnormalities in organelle morphology are largely manual an...
Plants Plant Cells Fluorescence Deep Learning Organelles Neural Networks, Computer Reproducibility of Results Computer-Aided Design
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Virtual organelle self-coding for fluorescence imaging via adversarial learning.

Journal of biomedical optics Sep 1, 2020
SIGNIFICANCE: Our study introduces an application of deep learning to virtually generate fluorescence images to reduce the burdens of cost and time from considerable effort in sample preparation related to chemical fixation and staining.
Neural Networks, Computer Organelles Signal-To-Noise Ratio Optical Imaging Image Processing, Computer-Assisted
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OrgaNet: A Robust Network for Subcellular Organelles Classification in Fluorescence Microscopy Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Automatic identification of subcellular compartments of proteins in fluorescence microscopy images is an important task to quantitatively evaluate cellular processes. A common problem for the development of deep learning based classifiers is that the...
Neural Networks, Computer Algorithms Humans Organelles Microscopy, Fluorescence HeLa Cells
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A reference library for assigning protein subcellular localizations by image-based machine learning.

The Journal of cell biology Mar 2, 2020
Confocal micrographs of EGFP fusion proteins localized at key cell organelles in murine and human cells were acquired for use as subcellular localization landmarks. For each of the respective 789,011 and 523,319 optically validated cell images, morph...
Green Fluorescent Proteins Recombinant Fusion Proteins Secretory Pathway Organelles Reference Standards Protein Transport Pattern Recognition, Automated Microscopy, Confocal Humans Cell Line Image Processing, Computer-Assisted Epithelial Cells Mice Machine Learning Mutation Animals
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