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US 11,900,600 B2Granted
Methods of analyzing microscopy images using machine learning
AssigneeAltius Institute for Biomedical Sciences
InventorsJohn A. Stamatoyannopoulos, Shreeram Akilesh, Alexander Muratov, Wouter Meuleman, William Kerwin
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Abstract
Disclosed herein are methods of utilizing machine learning methods to analyze microscope images of populations of cells.
Claims20
A method for characterizing a population of cells, the method comprising:
- a) acquiring a series of one or more images of a population of cells, wherein at least one image of the series comprises an image of one or more cells;
- and
- b) processing the series of one or more images using a statistical or machine learning algorithm, wherein the statistical or machine learning algorithm generates a cell characterization data set that comprises a basis representation of one or more key attributes of cells within the population of cells,
- wherein the statistical or machine learning algorithm comprises an unsupervised machine learning algorithm, and wherein the unsupervised machine learning algorithm comprises an artificial neural network, an association rule learning algorithm, a hierarchical clustering algorithm, a cluster analysis algorithm, a matrix factorization approach, a dimensionality reduction approach, or any combination thereof.
1 more element
A method for screening drug candidates, the method comprising:
- a) acquiring a series of one or more images of a population of cells both before and after contacting the cells with a drug candidate, wherein at least one image of the series comprises an image of one or more cells;
- b) separately processing the series of one or more images acquired before and after the contacting step using a statistical or machine learning algorithm, wherein the statistical or machine learning algorithm generates a cell characterization data set for each series that comprises a basis representation of one or more key attributes of cells within the population of cells;
- and
- c) comparing the cell characterization data set for the population of cells after contacting with the drug candidate to that for the population of cells before contacting with the drug candidate, wherein detection of a change in the cell characterization data set indicates that the drug candidate activates or inactivates an intracellular signaling pathway that affects at least one key attribute of cells within the population of cells.
1 more element
Dependencies are inferred from the claim text.