DETERMINING LABELS OF INHERITANCE DATASETS USING SIMULATED DATA INSTANCES

1 . A computer-implemented method for improving training of a machine learning model, the computer-implemented method comprising:

generating a plurality of reference panels for a plurality of data-inheritance origins, each reference panel corresponding to a data-inheritance origin corresponding to a population and comprising reference-panel datasets genetic markers representative of the data-inheritance origin;

constructing a plurality of simulated inheritance trees, wherein each simulated inheritance tree corresponds to a population and is built using the reference-panel datasets that are selected from the reference panel corresponding to the population, wherein building a simulated inheritance tree comprises simulating meiosis and recombination events based on the genetic markers of the reference-panel datasets to generate a plurality of simulated inheritance datasets of simulated descendant named entities;

generating plurality of simulated inheritance datasets representing a plurality of simulated named entities, each representing a simulated descendant named entity in one of the simulated inheritance trees; and

training a machine learning model that is configured to determine inheritance labels of an inheritance dataset, wherein training the machine learning model comprises:

initiating origin-specific weight parameters in the machine learning model;

applying the plurality of simulated inheritance datasets as training samples;

applying the machine learning model to predict inheritance labels of the training samples;

comparing predicted inheritance labels to actual labels obtained from the plurality of simulated inheritance trees; and

adjusting the origin-specific weight parameters based on label comparisons.

2 . The computer-implemented method of claim 1 , wherein generating the plurality of reference panels for the plurality of data-inheritance origins comprises:

filtering a plurality of candidates based on inheritance labels;

identifying, for each candidate, a number of the named entities in a particular data-inheritance origin whose inheritance datasets match the inheritance dataset of the candidate; and

selecting a candidate to be added to the reference-panel datasets based on the number of matched named entities that correspond to the candidate compared to numbers of matched named entities of other candidates.

3 . The computer-implemented method of claim 1 , wherein generating the plurality of reference panels for the plurality of data-inheritance origins comprises:

generating a candidate pool that include a plurality of candidates based on inheritance labels related to the particular origin;

determining that one of the candidates has a first number of matches associated with a particular origin and a second number of matches associated with a second origin, the second number of matches exceeding a threshold; and

removing said one of the candidates from the candidate pool.

4 . The computer-implemented method of claim 1 , wherein a particular simulated inheritance tree is associated with a geographical location, wherein constructing the particular simulated inheritance tree comprises:

accessing a population composition of the geographical location, the population composition comprising information related to percentage of named entities with the plurality of origins;

sampling, based on the population composition of the geographical location, reference-panel datasets from the plurality of reference panels for the plurality of origins; and

representing sampled reference-panel datasets in nodes of the particular simulated inheritance tree.

5 . The computer-implemented method of claim 4 , wherein the population composition comprising generation-specific composition, and wherein representing the sampled reference-panel datasets in the nodes of the particular simulated inheritance tree comprises:

selecting placements of the sampled reference-panel datasets based on the generation-specific composition.

6 . The computer-implemented method of claim 1 , wherein generating a particular simulated inheritance dataset representing a particular simulated named entity in a particular simulated inheritance tree comprises:

treating the particular simulated named entity as a descendant named entity of the reference-panel datasets that are placed in the particular simulated inheritance tree;

simulating a plurality of inheritance events; and

generating the particular simulated inheritance dataset of the particular simulated named entity based on the plurality of inheritance events.

7 . The computer-implemented method of claim 1 , wherein the machine learning model is a hidden Markov model with windows that represent segments of inheritance data, each window comprising a plurality of nodes and each node representing an origin, wherein the origin-specific weight parameters are associated with weights of the plurality of nodes.

8 . The computer-implemented method of claim 1 , wherein adjusting the origin-specific weight parameters comprises:

comparing the predicted inheritance labels to the actual labels to identifying under-represented origins and over-represented origins;

for an under-represented origin, increasing a value of the origin-specific weight parameter corresponding to the under-represented origin; and

for an over-represented origin, decreasing a value of the origin-specific weight parameter corresponding to the over-represented origin.

9 . The computer-implemented method of claim 1 , wherein the actual labels obtained from the plurality of simulated inheritance trees are generated by:

dividing a particular simulated inheritance dataset in a particular training sample into a plurality of windows;

examining how a segment of the particular simulated inheritance dataset in a particular window is inherited in a particular simulated inheritance tree;

identifying a reference-panel named entity in the particular inheritance tree who passes down the segment to the simulated inheritance dataset;

determining an origin label of said reference-panel named entity; and

using the origin label as the actual label.

10 . The computer-implemented method of claim 1 , wherein the training samples comprises admixed named entities that are simulated from plurality of simulated inheritance trees and non-admixed named entities that are sampled from actual user datasets.

11 . A system comprising:

one or more processors; and

memory configured to store instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform steps comprising:

generating a plurality of reference panels for a plurality of data-inheritance origins, each reference panel corresponding to a data-inheritance origin corresponding to a population and comprising reference-panel datasets genetic markers representative of the data-inheritance origin;

constructing a plurality of simulated inheritance trees, wherein each simulated inheritance tree corresponds to a population and is built using the reference-panel datasets that are selected from the reference panel corresponding to the population, wherein building a simulated inheritance tree comprises simulating meiosis and recombination events based on the genetic markers of the reference-panel datasets to generate a plurality of simulated inheritance datasets of simulated descendant named entities;

generating plurality of simulated inheritance datasets representing a plurality of simulated named entities, each representing a simulated descendant named entity in one of the simulated inheritance trees; and

training a machine learning model that is configured to determine inheritance labels of an inheritance dataset, wherein training the machine learning model comprises:

initiating origin-specific weight parameters in the machine learning model;

applying the plurality of simulated inheritance datasets as training samples;

applying the machine learning model to predict inheritance labels of the training samples;

comparing predicted inheritance labels to actual labels obtained from the plurality of simulated inheritance trees; and

adjusting the origin-specific weight parameters based on label comparisons.

12 . The system of claim 11 , wherein generating the plurality of reference panels for the plurality of data-inheritance origins comprises:

filtering a plurality of candidates based on inheritance labels;

identifying, for each candidate, a number of the named entities in a particular data-inheritance origin whose inheritance datasets match the inheritance dataset of the candidate; and

selecting a candidate to be added to the reference-panel datasets based on the number of matched named entities that correspond to the candidate compared to numbers of matched named entities of other candidates.

13 . The system of claim 11 , wherein generating the plurality of reference panels for the plurality of data-inheritance origins comprises:

generating a candidate pool that include a plurality of candidates based on inheritance labels related to the particular origin;

determining that one of the candidates has a first number of matches associated with a particular origin and a second number of matches associated with a second origin, the second number of matches exceeding a threshold; and

removing said one of the candidates from the candidate pool.

14 . The system of claim 11 , wherein a particular simulated inheritance tree is associated with a geographical location, wherein constructing the particular simulated inheritance tree comprises:

accessing a population composition of the geographical location, the population composition comprising information related to percentage of named entities with the plurality of origins;

sampling, based on the population composition of the geographical location, reference-panel datasets from the plurality of reference panels for the plurality of origins; and

representing sampled reference-panel datasets in nodes of the particular simulated inheritance tree.

15 . The system of claim 14 , wherein the population composition comprising generation-specific composition, and wherein representing the sampled reference-panel datasets in the nodes of the particular simulated inheritance tree comprises:

selecting placements of the sampled reference-panel datasets based on the generation-specific composition.

16 . The system of claim 11 , wherein generating a particular simulated inheritance dataset representing a particular simulated named entity in a particular simulated inheritance tree comprises:

treating the particular simulated named entity as a descendant named entity of the reference-panel datasets that are placed in the particular simulated inheritance tree;

simulating a plurality of inheritance events; and

generating the particular simulated inheritance dataset of the particular simulated named entity based on the plurality of inheritance events.

17 . The system of claim 11 , wherein the machine learning model is a hidden Markov model with windows that represent segments of inheritance data, each window comprising a plurality of nodes and each node representing an origin, wherein the origin-specific weight parameters are associated with weights of the plurality of nodes.

18 . The system of claim 11 , wherein adjusting the origin-specific weight parameters comprises:

comparing the predicted inheritance labels to the actual labels to identifying under-represented origins and over-represented origins;

for an under-represented origin, increasing a value of the origin-specific weight parameter corresponding to the under-represented origin; and

for an over-represented origin, decreasing a value of the origin-specific weight parameter corresponding to the over-represented origin.

19 . The system of claim 11 , wherein the actual labels obtained from the plurality of simulated inheritance trees are generated by:

dividing a particular simulated inheritance dataset in a particular training sample into a plurality of windows;

examining how a segment of the particular simulated inheritance dataset in a particular window is inherited in a particular simulated inheritance tree;

identifying a reference-panel named entity in the particular inheritance tree who passes down the segment to the simulated inheritance dataset;

determining an origin label of said reference-panel named entity; and

using the origin label as the actual label.

20 . A non-transitory computer readable medium for storing computer code comprising instructions, when executed by one or more computer processors, causing one or more computer processors to perform steps comprising:

generating a plurality of reference panels for a plurality of data-inheritance origins, each reference panel corresponding to a data-inheritance origin corresponding to a population and comprising reference-panel datasets genetic markers representative of the data-inheritance origin;

constructing a plurality of simulated inheritance trees, wherein each simulated inheritance tree corresponds to a population and is built using the reference-panel datasets that are selected from the reference panel corresponding to the population, wherein building a simulated inheritance tree comprises simulating meiosis and recombination events based on the genetic markers of the reference-panel datasets to generate a plurality of simulated inheritance datasets of simulated descendant named entities;

generating plurality of simulated inheritance datasets representing a plurality of simulated named entities, each representing a simulated descendant named entity in one of the simulated inheritance trees; and

training a machine learning model that is configured to determine inheritance labels of an inheritance dataset, wherein training the machine learning model comprises:

initiating origin-specific weight parameters in the machine learning model;

applying the plurality of simulated inheritance datasets as training samples;

applying the machine learning model to predict inheritance labels of the training samples;

comparing predicted inheritance labels to actual labels obtained from the plurality of simulated inheritance trees; and

adjusting the origin-specific weight parameters based on label comparisons.