System and method for training machine-learning algorithms for processing biology-related data, a microscope and a trained machine learning algorithm

A method for training machine-learning algorithms for processing biology-related data using a microscope, the method comprising:

• receiving biology-related language-based input training data, wherein the biology-related language-based input training data is a biological sequence;
• generating a first high-dimensional representation of the biology-related language-based input training data by a language recognition machine-learning algorithm, wherein the first high-dimensional representation comprises at least three entries each having a different value;
• generating biology-related language-based output training data based on the first high-dimensional representation by the language recognition machine-learning algorithm, wherein the biology-related language-based output training data comprises a prediction on a next element in the biological sequence;
• adjusting the language recognition machine-learning algorithm based on a comparison of the biology-related language-based input training data and the biology-related language-based output training data;
• receiving biology-related image-based input training data associated with the biology-related language-based input training data from the microscope;
• generating a second high-dimensional representation of the biology-related image-based input training data by a visual recognition machine-learning algorithm, wherein the second high-dimensional representation comprises at least three entries each having a different value;
• and
• adjusting the visual recognition machine-learning algorithm based on a comparison of the first high-dimensional representation and the second high-dimensional representation.

A non-transitory, computer-readable medium storing a trained machine learning algorithm, the trained machine learning algorithm trained by:

• receiving biology-related language-based input training data, wherein the biology-related language-based input training data is a biological sequence;
• generating a first high-dimensional representation of the biology-related language-based input training data by a language recognition machine-learning algorithm, wherein the first high-dimensional representation comprises at least three entries each having a different value;
• generating biology-related language-based output training data based on the first high-dimensional representation by the language recognition machine-learning algorithm, wherein the biology-related language-based output training data comprises a prediction on a next element in the biological sequence;
• adjusting the language recognition machine-learning algorithm based on a comparison of the biology-related language-based input training data and the biology-related language-based output training data;
• receiving biology-related image-based input training data associated with the biology-related language-based input training data from a microscope;
• generating a second high-dimensional representation of the biology-related image-based input training data by a visual recognition machine-learning algorithm, wherein the second high-dimensional representation comprises at least three entries each having a different value;
• and
• adjusting the visual recognition machine-learning algorithm based on a comparison of the first high-dimensional representation and the second high-dimensional representation.