DISTRIBUTING DIAGNOSTIC PREDICTION MODELS

1 . A computing system for distributing diagnostic prediction models comprising:

one or more memories having stored thereon computer-executable instructions that, when executed by one or more processors, cause the computing system to:

receive from an application provider:

one or more diagnostic prediction models, each model trained to generate a respective diagnostic prediction based upon genomic data; and

for the each model, adaptation factors used to transform a target genomic dataset to conform to a dataset-specific nature of a reference genomic dataset of the each model;

provide for display via a graphical user interface, one or more representations corresponding to the one or more diagnostic prediction models;

verify a diagnostic prediction model of the one or more diagnostic prediction models for an application consumer based upon receiving information corresponding to an application consumer genomic dataset;

authorize the diagnostic prediction model for distribution to the application consumer; and

provide the diagnostic prediction model and the corresponding adaptation factors to the application consumer.

2 . The computing system of claim 1 , the one or more memories having stored thereon instructions that, when executed, cause the computing system further to:

determine at least one relevant label in common, between the application consumer genomic dataset and the reference genomic dataset, for a gene and associated outcome.

3 . The computing system of claim 2 , wherein the gene and associated outcome is based upon risk genes and protective genes.

4 . The computing system of claim 1 , the one or more memories having stored thereon instructions that, when executed, cause the computing system further to:

determine pairs of genes which have at least one covariance between the application consumer genomic dataset and the reference genomic dataset.

5 . The computing system of claim 4 , wherein to determine pairs of genes which have at least one covariance, the one or more memories have stored thereon instructions that, when executed, cause the computing system further to:

determine at least one relevant label is not in common, between the application consumer genomic dataset and the reference genomic dataset, for a gene and associated outcome.

6 . The computing system of claim 4 , the one or more memories having stored thereon instructions that, when executed, cause the computing system to:

generate a cosine similarity between the application consumer genomic dataset and the reference genomic dataset.

7 . The computing system of claim 1 , the one or more memories having stored thereon instructions that, when executed, cause the computing system further to:

verify the diagnostic prediction model of the one or more diagnostic prediction models for a second application consumer based upon receiving information corresponding to a second application consumer genomic dataset;

authorize the diagnostic prediction model for distribution to the second application consumer; and

provide the diagnostic prediction model and the corresponding adaptation factors to the second application consumer.

8 . The computing system of claim 1 , wherein one or both of the application provider and the application consumer are affiliated with a biotechnology entity, an educational entity, a collaborator of a provider of the diagnostic prediction models, or a pharmaceutical entity.

9 . The computing system of claim 1 , wherein the reference genomic dataset is generated by a first set of sequencing equipment and the application consumer genomic dataset is generated by a second set of sequencing equipment.

10 . The computing system of claim 9 , wherein the reference genomic dataset and the application consumer genomic dataset have differences in characteristics due to their respective sets of sequencing equipment.

11 . The computing system of claim 1 , wherein the reference genomic dataset is a public dataset or a laboratory-specific dataset, and the application consumer genomic dataset is the public dataset or the laboratory-specific dataset.

12 . The computing system of claim 1 , wherein the diagnostic prediction model is at least one of a diagnosticator model, a prognosticator model, a subtyping model, or a predictive model.

13 . A computer-implemented method for distributing diagnostic prediction models comprising:

receiving, via one or more processors, from an application provider:

one or more diagnostic prediction models, each model trained to generate a respective diagnostic prediction based upon genomic data; and

for the each model, adaptation factors used to transform a target genomic dataset to conform to a dataset-specific nature of a reference genomic dataset of the each model;

providing, by the one or more processors for display via a graphical user interface, one or more representations corresponding to the one or more diagnostic prediction models;

verifying, by the one or more processors, a diagnostic prediction model of the one or more diagnostic prediction models for an application consumer based upon receiving information corresponding to an application consumer genomic dataset;

authorizing, by the one or more processors, the diagnostic prediction model for distribution to the application consumer; and

providing, by the one or more processors, the diagnostic prediction model and the corresponding adaptation factors to the application consumer.

14 . The computer-implemented method of claim 13 , further comprising:

determining, by the one or more processors, at least one relevant label in common, between the application consumer genomic dataset and the reference genomic dataset, for a gene and associated outcome.

15 . The computer-implemented method of claim 14 , wherein the gene and associated outcome is based upon risk genes and protective genes.

16 . The computer-implemented method of claim 13 , further comprising:

determining, by the one or more processors, pairs of genes which have at least one covariance between the application consumer genomic dataset and the reference genomic dataset.

17 . The computer-implemented method of claim 16 , wherein determining the pairs of genes which have at least one covariance further comprises:

determining, by the one or more processors, at least one relevant label is not in common, between the application consumer genomic dataset and the reference genomic dataset, for a gene and associated outcome.

18 . The computer-implemented method of claim 16 , further comprising:

generating, by the one or more processors, a cosine similarity between the application consumer genomic dataset and the reference genomic dataset.

19 . The computer-implemented method of claim 13 , further comprising:

verifying, by the one or more processors, the diagnostic prediction model of the one or more diagnostic prediction models for a second application consumer based upon receiving information corresponding to a second application consumer genomic dataset;

authorizing, by the one or more processors, the diagnostic prediction model for distribution to the second application consumer; and

providing, by the one or more processors, the diagnostic prediction model and the corresponding adaptation factors to the second application consumer.

20 . A non-transitory, tangible computer-readable medium storing machine-readable instructions for distributing diagnostic prediction models that, when executed by one or more processors, cause the one or more processors to:

receive from an application provider:

one or more diagnostic prediction models, each model trained to generate a respective diagnostic prediction based upon genomic data; and

for the each model, adaptation factors used to transform a target genomic dataset to conform to a dataset-specific nature of a reference genomic dataset of the each model;

provide for display via a graphical user interface, one or more representations corresponding to the one or more diagnostic prediction models;

verify a diagnostic prediction model of the one or more diagnostic prediction models for an application consumer based upon receiving information corresponding to an application consumer genomic dataset;

authorize the diagnostic prediction model for distribution to the application consumer; and

provide the diagnostic prediction model and the corresponding adaptation factors to the application consumer.