MAPPING METHOD AND APPARATUS

1 . An apparatus comprising processing circuitry configured to:

acquire convolutional neural network (CNN) layers; and

train a first mapping layer which connects to an input layer of the CNN layers, and a second mapping layer which connects to an output layer of the CNN layers, wherein the first mapping layer maps omics input data to N-dimensional data, and wherein the second mapping layer receives further N-dimensional data that is output by the CNN layers and maps the further N-dimensional data to omics output data;

wherein the training of the first mapping layer and the second mapping layer comprises fixing parameters of the CNN layers and minimizing a loss function, wherein the loss function is dependent on the input omics data and the output omics data.

2 . The apparatus of claim 1 , wherein the second mapping layer is an inverse of the first mapping layer.

3 . The apparatus of claim 1 , wherein the loss function is a reconstruction loss between the input omics data and the output omics data.

4 . The apparatus of claim 1 , wherein the CNN layers are layers of an auto-encoder.

5 . The apparatus of claim 1 , wherein the first mapping layer is parameterized as a fully dense layer.

6 . The apparatus of claim 1 , wherein the first mapping layer comprises a one-to-one mapping.

7 . The apparatus of claim 1 , wherein the processing circuitry is further configured to train the CNN layers to reconstruct spatially ordered data before the training of the first mapping layer and second mapping layer.

8 . The apparatus of claim 1 , wherein the processing circuitry is further configured to train a task-specific model to obtain a task-specific output from ordered N-dimensional data obtained using the first mapping layer.

9 . The apparatus of claim 8 , wherein the task-specific output comprises a classification or a prediction.

10 . The apparatus of claim 8 , wherein the task-specific output comprises at least one of a classification of a disease, a classification of a phenotype, a classification of one or more disease characteristics, a prediction of a treatment response, a prediction of survival, a prediction of recurrence.

11 . The apparatus of claim 1 , wherein the first mapping layer is simultaneously or subsequently optimized based on an error derived from a task performed by a task-specific model.

12 . The apparatus of claim 1 , wherein the processing circuitry is further configured to display ordered N-dimensional data obtained using the first mapping layer.

13 . The apparatus of claim 11 , wherein the processing circuitry is further configured to highlight in the ordered N-dimensional data regions of data that are relevant to one or more task-specific outputs.

14 . The apparatus of claim 1 , wherein the first mapping layer is initialized using domain knowledge.

15 . The apparatus of claim 1 , wherein the training of the first mapping layer is initialized using a two-dimensional image format obtained by a method comprising:

receiving omics data, the omics data comprising a plurality of values, wherein each value of the plurality of values is associated with a corresponding biomolecule of a plurality of biomolecules;

calculating a respective distance between each pair of biomolecules from the plurality of biomolecules;

applying a manifold learning method to the distances to obtain a respective position in a two-dimensional space mapped to each biomolecule of the plurality of biomolecules;

adjusting the positions to achieve a more even distribution of the positions over the two-dimensional space; and

storing a two-dimensional image format of display positions for each biomolecule of the plurality of biomolecules based on the adjusted positions.

16 . The apparatus of claim 1 , wherein the omics data comprises at least one of transcriptome data, proteome data, metabolome data, or gene mutational data.

17 . A method comprising:

acquiring convolutional neural network (CNN) layers; and

training a first mapping layer which connects to an input layer of the CNN layers, and a second mapping layer which connects to an output layer of the CNN layers, wherein the first mapping layer maps omics input data to N-dimensional data, and wherein the second mapping layer receives further N-dimensional data that is output by the CNN layers and maps the further N-dimensional data to omics output data;

wherein the training of the first mapping layer and the second mapping layer comprises fixing parameters of the CNN layers and minimizing a loss function, wherein the loss function is dependent on the input omics data and the output omics data.

18 . An apparatus comprising processing circuitry configured to:

obtain a trained first mapping layer which maps omics input data to N-dimensional data, wherein the first mapping layer is trained in accordance with the method of claim 17 ;

use the trained first mapping layer to transform a set of omics input data into a set of spatially ordered data; and

apply a task-specific model to the spatially ordered data to obtain a task-specific output.

19 . An apparatus according to claim 18 , wherein the task-specific output comprises a classification or a prediction.

20 . A method comprising:

obtaining a trained first mapping layer which maps omics input data to N-dimensional data, wherein the first mapping layer is trained in accordance with the method of claim 17 ;

using the trained first mapping layer to transform a set of omics input data into a set of spatially ordered data; and

applying a task-specific model to the spatially ordered data to obtain a task-specific output.