ETHNICITY PREDICTION WITH STRING KERNAL MODEL

1 . A computer-implemented method, comprising:

receiving a target inheritance dataset of a target named entity;

receiving a plurality of reference inheritance datasets corresponding to a plurality of reference named entities, each reference inheritance dataset belonging to a reference named entity;

generating a feature vector corresponding to the target inheritance dataset, wherein generating the feature vector comprises:

applying a string kernel model to matched data strings between the target inheritance dataset and each of the reference inheritance datasets, wherein the string kernel model determines a similarity metric based on a polynomial value for contiguous matched sites of the matched data strings between the target inheritance dataset and each of the reference inheritance datasets;

generating the feature vector based on results of applying the string kernel model to the matched data strings between the target inheritance dataset and the plurality of reference inheritance datasets;

applying a decision tree model to the feature vector corresponding to the target inheritance dataset; and

generating an output using the decision tree model, the output providing information associated with a data classification of the target named entity.

2 . The method of claim 1 , wherein generating a feature vector further comprises:

dividing the target inheritance dataset and the plurality of reference inheritance datasets into a plurality of reference regions, each reference region comprising a sequence of single nucleotide polymorphisms (SNPs); and

identifying the matched data strings between the target inheritance dataset and each of the reference inheritance datasets in each reference region.

3 . The method of claim 1 , wherein identifying the matched data strings comprises:

for each reference region, comparing each of SNPs sites in the target inheritance dataset with corresponding SNPs sites in each of the reference inheritance datasets.

4 . The method of claim 1 , wherein applying a string kernel model to matched data strings comprises:

determining, based on the matched data strings, a plurality of similarity scores between the target inheritance dataset and the plurality of reference inheritance datasets, each similarity score indicating a level of similarity between the target inheritance dataset and one of the plurality of reference inheritance dataset;

obtaining a similarity score distribution based on the determined plurality of similarity scores for the target inheritance dataset; and

selecting one or more statistical parameters associated with the similarity score distribution as features of the feature vector.

5 . The method of claim 1 , wherein the string kernel model includes one or more parameters associated with weight assigned to the contiguous matched sites of the matched data strings.

6 . The method of claim 5 , wherein the string kernel model assigns a higher weight to a matched data string with more contiguous matched sites than a matched data string with less contiguous sites.

7 . The method of claim 1 , wherein the decision tree model includes XGBoost.

8 . A computer-readable storage medium having stored thereon computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations, comprising:

receiving a target haplotype dataset of a target individual;

receiving a plurality of reference haplotype datasets corresponding to a plurality of reference named entities, each reference haplotype dataset belonging to a reference named entity;

generating a feature vector corresponding to the target haplotype dataset, wherein generating the feature vector comprises:

applying a string kernel model to matched data strings between the target haplotype dataset and each of the reference haplotype datasets, wherein the string kernel model determines a similarity metric based on a polynomial value for contiguous matched sites of the matched data strings between the target haplotype dataset and each of the reference haplotype datasets;

generating the feature vector based on results of applying the string kernel model to the matched data strings between the target haplotype dataset and the plurality of reference haplotype datasets;

applying a decision tree model to the feature vector corresponding to the target haplotype dataset; and

generating an output using the decision tree model, the output providing information associated with a data classification of the target named entity.

9 . The computer-readable storage medium of claim 8 , wherein generating a feature vector further comprises:

dividing the target haplotype dataset and the plurality of reference haplotype datasets into a plurality of reference regions, each reference region comprising a sequence of single nucleotide polymorphisms (SNPs); and

identifying the matched data strings between the target haplotype dataset and each of the reference haplotype datasets in each reference region.

10 . The computer-readable storage medium of claim 8 , wherein identifying the matched data strings comprises:

for each reference region, comparing each of SNPs sites in the target haplotype dataset with corresponding SNPs sites in each of the reference haplotype datasets.

11 . The computer-readable storage medium of claim 8 , wherein applying a string kernel model to matched data strings comprises:

determining, based on the matched data strings, a plurality of similarity scores between the target haplotype dataset and the plurality of reference haplotype datasets, each similarity score indicating a level of similarity between the target haplotype dataset and one of the plurality of reference haplotype dataset;

obtaining a similarity score distribution based on the determined plurality of similarity scores for the target haplotype dataset; and

selecting one or more statistical parameters associated with the similarity score distribution as features of the feature vector.

12 . The computer-readable storage medium of claim 8 , wherein the string kernel model includes one or more parameters associated with weight assigned to the contiguous matched sites of the matched data strings.

13 . The computer-readable storage medium of claim 12 , wherein the string kernel model assigns a higher weight to a matched data string with more contiguous matched sites than a matched data string with less contiguous sites.

14 . The computer-readable storage medium of claim 8 , wherein the decision tree model includes XGBoost.

15 . A computer system, comprising:

one or more processors; and

a hardware storage device having stored thereon computer-executable instructions that, when executed by the one or more processors, causes the computer system to perform operations, comprising:

receiving a target inheritance dataset of a target named entity;

receiving a plurality of reference inheritance datasets corresponding to a plurality of reference named entities, each reference inheritance dataset belonging to a reference named entity;

generating a feature vector corresponding to the target inheritance dataset, wherein generating the feature vector comprises:

applying a string kernel model to matched data strings between the target inheritance dataset and each of the reference inheritance datasets, wherein the string kernel model determines a similarity metric based on a polynomial value for contiguous matched sites of the matched data strings between the target inheritance dataset and each of the reference inheritance datasets;

generating the feature vector based on results of applying the string kernel model to the matched data strings between the target inheritance dataset and the plurality of reference inheritance datasets;

applying a decision tree model to the feature vector corresponding to the target inheritance dataset; and

generating an output using the decision tree model, the output providing information associated with a data classification of the target named entity.

16 . The computer system of claim 15 , wherein generating a feature vector further comprises:

dividing the target inheritance dataset and the plurality of reference inheritance datasets into a plurality of reference regions, each reference region comprising a sequence of single nucleotide polymorphisms (SNPs); and

identifying the matched data strings between the target inheritance dataset and each of the reference inheritance datasets in each reference region.

17 . The computer system of claim 15 , wherein identifying the matched data strings comprises:

for each reference region, comparing each of SNPs sites in the target inheritance dataset with corresponding SNPs sites in each of the reference inheritance datasets.

18 . The computer system of claim 15 , wherein applying a string kernel model to matched data strings comprises:

determining, based on the matched data strings, a plurality of similarity scores between the target inheritance dataset and the plurality of reference inheritance datasets, each similarity score indicating a level of similarity between the target inheritance dataset and one of the plurality of reference inheritance dataset;

obtaining a similarity score distribution based on the determined plurality of similarity scores for the target inheritance dataset; and

selecting one or more statistical parameters associated with the similarity score distribution as features of the feature vector.

19 . The computer system of claim 15 , wherein the string kernel model includes one or more parameters associated with weight assigned to the contiguous matched sites of the matched data strings.

20 . The computer system of claim 19 , wherein the string kernel model assigns a higher weight to a matched data string with more contiguous matched sites than a matched data string with less contiguous sites.