Predictive data analysis using image representations of genomic data

Patent metadata

Field	Value
Publication number	US-2022358697-A1
Application number	US-202117473064-A
Country	US
Kind code	A1
Filing date	Sep 13, 2021
Priority date	May 10, 2021
Publication date	Nov 10, 2022
Grant date	—

Abstract

There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating image representations of genetic variant data. In one example, embodiments comprise receiving an input feature, generating one or more image representations of the input feature, generating a tensor representation of the one or more image representations, generating a plurality of positional encoding maps, generating an image-based prediction based at least in part on the image representation, and performing one or more prediction-based actions based at least in part on the image-based prediction.

First claim

What is claimed is:

1 . A computer-implemented method for dynamically generating an image-based prediction, the computer-implemented method comprising:
receiving, using one or more processors, an input feature, wherein the input feature comprises one or more feature values, wherein each feature value of the one or more feature values corresponds to a genetic variant identifier, and wherein each feature value is associated with an input feature type designation of a plurality of input feature type designations; generating, using the one or more processors, one or more image representations of the input feature, wherein: (i) an image representation count of the one or more image representations is based at least in part on the plurality of input feature type designations (ii) each image representation of the one or more image representations comprises a plurality of image regions, (iii) each image region for an image representation corresponds to a genetic variant identifier, and (iv) generating each of the one or more image representations associated with a character category is performed based at least in part on the one or more feature values of the input feature having the input feature type designation; generating, using the one or more processors, a tensor representation of the one or more image representations of the input feature; generating, using the one or more processors, a plurality of positional encoding maps, wherein: (i) each positional encoding map of the one or more positional encoding maps comprises a plurality of positional encoding map regions, (ii) each positional encoding map region for a positional encoding map corresponds to a genetic variant identifier, (iii) each genetic variant identifier is associated with a positional encoding map region set comprising each positional encoding map region associated with the genetic variant identifier across the plurality of positional encoding maps, and (iv) each positional encoding map region set for a genetic variant identifier represents a the genetic variant identifier; generating, using the one or more processors, an image-based prediction based at least in part on the tensor representation of the one or more image representations of the input feature and the one or more positional encoding maps; and performing, using the one or more processors, one or more prediction-based actions based at least in part on the image-based prediction.

2 . The method of claim 1 , wherein generating the one or more image representations of the input feature further comprises:
generating, using the one or more processors, a first image representation generated based at least in part on a first subset of input features; generating, using the one or more processors, a second image representation generated based at least in part on a second subset of input feature; and generating, using the one or more processors, a differential image representation of the one or more image representations based at least in part on performing an image difference operation across the first image representation and the second image representation.

3 . The method of claim 1 , wherein generating the one or more image representations of the input feature further comprises:
generating, using the one or more processors, a first allele image representation generated based at least in part on a subset of the input features corresponding to a first allele; generating, using the one or more processors, a second allele image representation generated based at least in part on a subset of the input feature corresponding to a second allele; generating, using the one or more processors, a dominant allele image representation generated based at least in part on a subset of the input feature corresponding to a dominant allele; generating, using the one or more processors, a minor allele image representation generated based at least in part on a subset of the input feature corresponding to a minor allele; and generating, using the one or more processors, a zygosity image representation of the one or more image representations based at least in part on performing one or more operations across the first allele image representation, the second allele image representation, the dominant allele image representation, and the minor allele image representation.

4 . The method of claim 1 , wherein generating the one or more image representations of the input feature further comprises:
identifying one or more initial image representations of the input feature; assigning, using the one or more processors, one or more intensity values to each input feature type designation of the plurality of input feature type designations; generating, using the one or more processors, one or more intensity image representations of the one or more initial image representations, wherein (i) each image representation of the one or more intensity image representations comprises a plurality of intensity image regions, (ii) each image region for an intensity image representation corresponds to a genetic variant identifier, and (iii) generating the one or more intensity image representations is determined based at least in part on the one or more feature values and the assigned intensity value for each input feature type designation.

5 . The method of claim 1 , wherein the image-based prediction comprises generating, using the one or more processors, a polygenic risk score for one or more diseases for one or more individuals associated with the input feature.

6 . The method of claim 1 , wherein each feature value of the one or more feature values corresponds to a categorical feature type or numerical feature type.

7 . The method of claim 1 , wherein each feature value of the one or more feature values further corresponds to a chromosome number and locus.

8 . An apparatus for dynamically generating an image-based prediction, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the processor, cause the apparatus to at least:
receive an input feature, wherein the input feature comprises one or more feature values, wherein each feature value of the one or more feature values corresponds to a genetic variant identifier, and wherein each feature value is associated with an input feature type designation of a plurality of input feature type designations; generate one or more image representations of the input feature, wherein: (i) an image representation count of the one or more image representations is based at least in part on the plurality of input feature type designations (ii) each image representation of the one or more image representations comprises a plurality of image regions, (iii) each image region for an image representation corresponds to a genetic variant identifier, and (iv) generating each of the one or more image representations associated with a character category is performed based at least in part on the one or more feature values of the input feature having the input feature type designation; generate a tensor representation of the one or more image representations of the input feature; generate a plurality of positional encoding maps, wherein: (i) each positional encoding map of the one or more positional encoding maps comprises a plurality of positional encoding map regions, (ii) each positional encoding map region for a positional encoding map corresponds to a genetic variant identifier, (iii) each genetic variant identifier is associated with a positional encoding map region set comprising each positional encoding map region associated with the genetic variant identifier across the plurality of positional encoding maps, and (iv) each

Assignees

• Optum Services Ireland Ltd

Inventors

• Selim Ahmed
• Godden Paul J
• Bridges Michael

Classifications

• G06T11/00

  Two-dimensional [2D] image generation · CPC title

• G06T11/26 Primary

  Drawing of charts or graphs · CPC title

• G06T7/0012

  Biomedical image inspection · CPC title

• G16B5/20

  Probabilistic models · CPC title

• G06T2207/20081

  Training; Learning · CPC title