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 language-based search data, wherein the biology-related language-based search data is at least one of a nucleotide sequence, a protein sequence, a description of a biological molecule or biological structure, a description of a behavior of a biological molecule or biological structure, or a description of a biological function or a biological activity;

generate a first high-dimensional representation of the biology-related language-based search data by a trained language 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 the system is configured to select the trained language recognition machine-learning algorithm from a plurality of trained language recognition machine-learning algorithms based on the biology-related language-based search data;

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;

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

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.

2. 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.

3. 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.

4. 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.

5. The system of claim 1 , wherein the system is configured to output at least one of the closest second high-dimensional representation, the 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 the 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.

6. 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.

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

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

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

10. The system of claim 1 , wherein more than 50% of values of the entries of the first high-dimensional representation and more than 50% of values of the entries of the second high-dimensional representations are unequal 0.

11. 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.

12. The system of claim 1 , wherein the biology-related language-based search data comprises a length of more than 20 characters.

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

14. The system of claim 13 , wherein the trained language recognition neural network comprises more than 30 layers.

15. The system of claim 13 , wherein the trained language recognition neural network is a recurrent neural network.

16. The system of claim 13 , wherein the trained language recognition neural network is a long short-term memory network.

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

the second high-dimensional representations of the plurality of second high-dimensional representations of the plurality of biology-related image-based input data sets using a trained visual recognition machine-learning algorithm; or

the second high-dimensional representations of the plurality of second high-dimensional representations of the plurality of biology-related language-based input data sets using a trained language recognition machine-learning algorithm,

wherein each second high-dimensional representation of the plurality of second high-dimensional representations comprises at least 3 entries each having a different value.

18. The system of claims 17 , wherein the trained visual recognition machine-learning algorithm comprises a trained visual recognition neural network.

19. The system of claim 18 , wherein the trained visual recognition neural network comprises more than 30 layers.

20. The system of claim 18 , wherein the trained visual recognition neural network is a convolutional neural network or a capsule network.

21. The system of claim 18 , wherein the trained visual recognition neural network comprises a plurality of convolution layers and a plurality of pooling layers.

22. The system of claim 18 , wherein the trained visual recognition neural network uses a rectified linear unit activation function.

23. The system of claim 17 , wherein the trained visual recognition machine-learning algorithm or the trained language recognition machine-learning algorithm for generating the plurality of second high-dimensional representations and the trained language recognition machine-learning algorithm for generating the first high-dimensional representation are all trained on an identical embedding space.

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

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

generate a first high-dimensional representation of the second biology-related language-based search data by the trained language 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 language-based search data and the first high-dimensional representation of the second biology-related language-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.

25. The system of claim 24 , 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 language-based search data and the first high-dimensional representation of the second biology-related language-based search data.

26. A microscope comprising a system of claim 1 .

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

receive language-based search data;

generate a first high-dimensional representation of the language-based search data by a trained language 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 the system is configured to select the trained language recognition machine-learning algorithm from a plurality of trained language recognition machine-learning algorithms based on the language-based search data;

obtain a plurality of second high-dimensional representations of a plurality of image-based input data sets;

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;

provide a control signal for controlling an operation of a microscope based on the selected second high-dimensional representation.

28. The system of claim 27 , wherein the system is configured to 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, wherein the control signal is configured to trigger the microscope to drive to the microscope target position.

29. The system of claim 27 , wherein the system is configured to generate the plurality of second high-dimensional representations of the plurality of image-based input data sets by a visual recognition machine-learning algorithm executed by the one or more processors.

30. The system of claim 27 , 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.

31. The system of claim 27 , further comprising the microscope configured to take a plurality of images of a specimen, wherein the plurality of image-based input data sets represents the plurality of images of the specimen.

32. A method for processing biology-related language-based search data, the method comprising:

receiving biology-related language-based search data, wherein the biology-related language-based search data is at least one of a nucleotide sequence, a protein sequence, a description of a biological molecule or biological structure, a description of a behavior of a biological molecule or biological structure, or a description of a biological function or a biological activity;

generating a first high-dimensional representation of the biology-related language-based search data by a trained language recognition machine-learning algorithm, wherein the first high-dimensional representation comprises at least 3 entries each having a different value,

wherein the trained language recognition machine-learning algorithm is selected from a plurality of trained language recognition machine-learning algorithms based on the biology-related language-based search data;

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; and

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

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

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

receiving language-based search data;

generating a first high-dimensional representation of the language-based search data by a trained language recognition machine-learning algorithm, wherein the first high-dimensional representation comprises at least 3 entries each having a different value,

wherein the trained language recognition machine-learning algorithm is selected from a plurality of trained language recognition machine-learning algorithms based on the language-based search data;

obtaining a plurality of second high-dimensional representations of a plurality of image-based input data sets;

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; and

controlling an operation of a microscope based on the selected second high-dimensional representation.