System and method for processing biology-related data, a system and method for controlling a microscope and a microscope

1. A system comprising one or more processors and one or more storage devices, wherein the system is configured to:

receive biology-related image-based search data from the one or more storage devices;

generate a first high-dimensional representation of the biology-related image-based search data by a trained visual recognition machine-learning algorithm executed by the one or more processors, wherein the first high-dimensional representation comprises at least 3 entries each having a different value, wherein more than 50% of values of the entries of the first high-dimensional representation are unequal 0;

obtain a plurality of second high-dimensional representations of a plurality of biology-related image-based input data sets or of a plurality of biology-related language-based input data sets, wherein more than 50% of values of entries of the second high-dimensional representations are unequal 0;

compare the first high-dimensional representation to each second high-dimensional representation of the plurality of second high-dimensional representations;

select a closest second high-dimensional representation of the plurality of second high-dimensional representations closest to the first high-dimensional representation based on the comparison; and

output to a database at least one of:

the closest second high-dimensional representation,

a biology-related image-based input data set of the plurality of biology-related image-based input data sets, which corresponds to the closest second high-dimensional representation, or

a biology-related language-based input data set of the plurality of biology-related language-based input data sets, which corresponds to the closest second high-dimensional representation.

2. The system of claim 1 , wherein the biology-related image-based search data is image data of an image of at least one of a biological structure comprising a nucleotide sequence, a biological structure comprising a protein sequence, a biological molecule, bio-logical tissue, a biological structure with a specific behavior, or a biological structure with a specific biological function or a specific biological activity.

3. The system of claim 1 , wherein the values of one or more entries of the first high-dimensional representation are proportional to a likelihood of a presence of a specific biological function or a specific biological activity.

4. The system of claim 1 , wherein the values of one or more entries of the second high-dimensional representations are proportional to a likelihood of a presence of a specific biological function or a specific biological activity.

5. The system of claim 1 , wherein the comparison of the first high-dimensional representation with each second high-dimensional representation of the plurality of second high-dimensional representations is based on an Euclidean distance function or an earth mover's distance function.

6. The system of claim 1 , wherein the first high-dimensional representation and the second high-dimensional representations are numerical representations.

7. The system of claim 1 , wherein the first high-dimensional representation and the second high-dimensional representations comprise each more than 100 dimensions.

8. The system of claim 1 , wherein the first high-dimensional representation is a first vector and the second high-dimensional representations are second vectors.

9. The system of claim 1 , wherein the values of more than 5 entries of the first high-dimensional representation are larger than 10% of a largest absolute value of the entries of the first high-dimensional representation and the values of more than 5 entries of each second high-dimensional representation of the plurality of second high-dimensional representations are larger than 10% of a respective largest absolute value of the entries of the second high-dimensional representations.

10. The system of claim 1 , wherein the trained visual recognition machine-learning algorithm comprises a trained visual recognition neural network.

11. The system of claim 10 , wherein the trained visual recognition neural network comprises more than 30 layers.

12. The system of claim 10 , wherein the trained visual recognition neural network is a convolutional neural network or a capsule network.

13. The system of claim 10 , wherein the trained visual recognition neural network comprises a plurality of convolution layers and a plurality of pooling layers.

14. The system of claim 10 , wherein the trained visual recognition neural network uses a rectified linear unit activation function.

15. The system of claim 1 , wherein the system is configured to obtain the second high-dimensional representations by generating the second high-dimensional representations of the plurality of second high-dimensional representations of the plurality of biology-related image-based input data sets or the plurality of biology-related language-based input data sets by the trained visual recognition machine-learning algorithm executed by the one or more processors, wherein each second high-dimensional representation of the plurality of second high-dimensional representations comprises at least 3 entries each having a different value.

16. The system of claim 1 , further comprising a microscope configured to obtain the plurality of biology-related image-based input data sets by taking images of a biological specimen.

17. The system of claim 1 , wherein the system is configured to select the trained visual recognition machine-learning algorithm from a plurality of trained visual recognition machine-learning algorithms based on the biology-related im-age-based search data.

18. The system of claim 1 , wherein the system is configured to:

receive second biology-related image-based search data and information on a logical operator;

generate a first high-dimensional representation of the second biology-related image-based search data by the trained visual recognition machine-learning algorithm executed by the one or more processors;

determine a combined high-dimensional representation based on a combination of the first high-dimensional representation of the first biology-related image-based search data and the first high-dimensional representation of the second biology-related image-based search data according to the logical operator; and

compare the combined high-dimensional representation to each second high-dimensional representation of the plurality of second high-dimensional representations.

19. The system of claim 18 , wherein the logical operator is an AND-operator and the combined high-dimensional representation is determined by adding the first high-dimensional representation of the first biology-related image-based search data and the first high-dimensional representation of the second biology-related image-based search data.

20. A microscope comprising a system of claim 1 .

21. A system comprising one or more processors and one or more storage devices, wherein the system is configured to:

receive image-based search data;

generate a first high-dimensional representation of the image-based search data by a trained visual recognition machine-learning algorithm executed by the one or more processors, wherein the first high-dimensional representation comprises at least 3 entries each having a different value, wherein more than 50% of values of the entries of the first high-dimensional representation are unequal 0;

obtain a plurality of second high-dimensional representations of a plurality of image-based input data sets, wherein more than 50% of values of entries of the second high-dimensional representations are unequal 0;

select a second high-dimensional representation from the plurality of second high-dimensional representations based on a comparison of the first high-dimensional representation with each second high-dimensional representation of the plurality of second high-dimensional representations;

determine a microscope target position based on the selected second high-dimensional representation, wherein the microscope target position is a position at which an image was taken, which was represented by the image-based input data, which corresponds to the selected second high-dimensional representation; and

control an operation of a microscope based on the selected second high-dimensional representation, wherein the microscope is driven to the microscope target position.

22. The system of claim 21 , wherein the system is configured to generate the plurality of second high-dimensional representations of the plurality of im-age-based input data sets by a visual recognition machine-learning algorithm executed by the one or more processors.

23. The system of claim 21 , wherein the system is configured to select a second high-dimensional representation of the plurality of second high-dimensional representations closest to the first high-dimensional representation based on the comparison.

24. The system of claim 21 , further comprising the microscope configured to take a plurality of images of a specimen, wherein the plurality of image-based in-put data sets represents the plurality of images of the specimen.

25. A system comprising one or more processors and one or more storage devices, wherein the system is configured to:

determine a plurality of clusters of a plurality of second high-dimensional representations of a plurality of image-based input data sets by a clustering algorithm executed by the one or more processors, wherein more than 50% of values of entries of the second high-dimensional representations are unequal 0;

determine a first high-dimensional representation of a cluster center of the cluster of the plurality of clusters, wherein more than 50% of values of entries of the first high-dimensional representation are unequal 0;

select a second high-dimensional representation from the plurality of second high-dimensional representations based on a comparison of the first high-dimensional representation with each or a subset of the second high-dimensional representations of the plurality of second high-dimensional representations;

determine a microscope target position based on the selected second high-dimensional representation, wherein the microscope target position is a position at which an image was taken, which was represented by the image-based input data, which corresponds to the selected second high-dimensional representation; and

control an operation of a microscope based on the selected second high-dimensional representation, wherein the microscope is driven to the microscope target position.

26. The system of claim 25 , wherein the clustering algorithm comprises a k-means clustering algorithm or a mean shift clustering algorithm.

27. A method for processing biology-related image-based search data, the method comprising:

receiving biology-related image-based search data from one or more storage devices;

generating a first high-dimensional representation of the biology-related image-based search data by a trained visual recognition machine-learning algorithm, wherein the first high-dimensional representation comprises at least 3 entries each having a different value, wherein more than 50% of values of the entries of the first high-dimensional representation are unequal 0;

obtaining a plurality of second high-dimensional representations of a plurality of biology-related image-based input data sets or a plurality of biology-related language-based input data sets, wherein more than 50% of values of entries of the second high-dimensional representations are unequal 0;

comparing the first high-dimensional representation with each second high-dimensional representation of the plurality of second high-dimensional representations;

selecting a closest second high-dimensional representation of the plurality of second high-dimensional representations closest to the first high-dimensional representation based on the comparison; and

outputting to a database at least one of:

the closest second high-dimensional representation,

a biology-related image-based input data set of the plurality of biology-related image-based input data sets, which corresponds to the closest second high-dimensional representation, or

a biology-related language-based input data set of the plurality of biology-related language-based input data sets, which corresponds to the closest second high-dimensional representation.

28. A method for controlling a microscope, the method comprising:

receiving image-based search data;

generating a first high-dimensional representation of the image-based search data by a trained visual recognition machine-learning algorithm, wherein the first high-dimensional representation comprises at least 3 entries each having a different value, wherein more than 50% of values of the entries of the first high-dimensional representation are unequal 0;

obtaining a plurality of second high-dimensional representations of a plurality of image-based input data sets, wherein more than 50% of values of entries of the second high-dimensional representations are unequal 0;

selecting a second high-dimensional representation from the plurality of second high-dimensional representations based on a comparison of the first high-dimensional representation with each second high-dimensional representation of the plurality of second high-dimensional representations;

determining a microscope target position based on the selected second high-dimensional representation, wherein the microscope target position is a position at which an image was taken, which was represented by the image-based input data, which corresponds to the selected second high-dimensional representation; and

controlling an operation of a microscope based on the selected second high-dimensional representation, wherein the microscope is driven to the microscope target position.

29. A method for controlling a microscope, the method comprising:

determining a plurality of clusters of a plurality of second high-dimensional representations of a plurality of image-based input data sets by a clustering algorithm, wherein more than 50% of values of entries of the second high-dimensional representations are unequal 0;

determining a first high-dimensional representation of a cluster center of the cluster of the plurality of clusters, wherein more than 50% of values of entries of the first high-dimensional representation are unequal 0;

selecting a second high-dimensional representation from the plurality of second high-dimensional representations based on a comparison of the first high-dimensional representation with each or a subset of the second high-dimensional representations of the plurality of second high-dimensional representations;

determining a microscope target position based on the selected second high-dimensional representation, wherein the microscope target position is a position at which an image was taken, which was represented by the image-based input data, which corresponds to the selected second high-dimensional representation; and

controlling an operation of a microscope based on the selected second high-dimensional representation, wherein the microscope is driven to the microscope target position.