Methods and systems for predicting neurodegenerative disease state
AssigneeNew York Stem Cell Foundation, Inc.
InventorsBjarki Johannesson, Bianca Migliori, Frederick J. Monsma, Jr., Scott Noggle, Daniel Paull
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Abstract
The present disclosure provides automated methods and systems for implementing a pipeline involving the training and deployment of a predictive model for predicting cellular diseased state (e.g., neurodegenerative disease state such as presence or absence of Parkinson's Disease). Such a predictive model distinguishes between morphological cellular phenotypes e.g., morphological cellular phenotypes elucidated using Cell Paint, exhibited by cells of different diseased states.
Claims20
A method comprising:
- obtaining or having obtained a cell;
- capturing one or more images of the cell;
- and
- analyzing the one or more images using a predictive model to predict a neurodegenerative disease state of the cell, the predictive model trained to distinguish between morphological profiles of cells of different neurodegenerative disease states, and the morphological profile being extracted from a layer of a deep learning neural network or being an embedding representing a dimensionally reduced representation of values of the layer of the deep learning neural network.
A non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to:
- capture one or more images of a cell;
- and
- analyze the one or more images using a predictive model to predict a neurodegenerative disease state of the cell, the predictive model trained to distinguish between morphological profiles of cells of different neurodegenerative disease states, and the morphological profile being extracted from a layer of a deep learning neural network or being an embedding representing a dimensionally reduced representation of values of the layer of the deep learning neural network.
Dependencies are inferred from the claim text.