Machine learning models for determining pathogenic genetic variants

A computer-implemented method comprising:

• invoking, using an application program interface (API), a data crawler to identify research publications related to human genomes of the particular population, wherein each of the plurality of research publications refers to a respective genetic variant of a plurality of genetic variants;
• for each of the plurality of research publications,
• analyzing content of the research publication to determine whether the respective genetic variant is classified as a pathogenic genetic variant according to the research publication, and
• in response to determining that the respective genetic variant is classified as the pathogenic genetic variant, (i) adding the respective genetic variant to a current set of pathogenic genetic variants, (ii) determining, from the content of the research publication, a phenotype that the respective genetic variant is linked to, and (ii) determining one or more characteristics of the research publication;
• for each pathogenic genetic variant in the current set of pathogenic genetic variants, assigning, using a machine learning model, the respective importance score to the pathogenic genetic variant based on characteristics of research publications from which the pathogenic genetic variant is determined, wherein the respective importance score represents (i) a level of importance of the pathogenic genetic variant to the particular population and (ii) a level of contribution of the pathogenic genetic variant to the phenotype that the pathogenic genetic variant is linked to;
• ranking, using the machine learning model, the pathogenic genetic variants in the current set according to the respective importance scores;
• and
• storing the ranked pathogenic genetic variants in a variant database.

One or more non-transitory computer storage media storing instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising:

• invoking, using an application program interface (API), a data crawler to identify research publications related to human genomes of the particular population, wherein each of the plurality of research publications refers to a respective genetic variant of a plurality of genetic variants;
• for each of the plurality of research publications,
• analyzing content of the research publication to determine whether the respective genetic variant is classified as a pathogenic genetic variant according to the research publication, and
• in response to determining that the respective genetic variant is classified as the pathogenic genetic variant, (i) adding the respective genetic variant to a current set of pathogenic genetic variants, (ii) determining, from the content of the research publication, a phenotype that the respective genetic variant is linked to, and (ii) determining one or more characteristics of the research publication;
• for each pathogenic genetic variant in the current set of pathogenic genetic variants, assigning, using a machine learning model, the respective importance score to the pathogenic genetic variant based on characteristics of research publications from which the pathogenic genetic variant is determined, wherein the respective importance score represents (i) a level of importance of the pathogenic genetic variant to the particular population and (ii) a level of contribution of the pathogenic genetic variant to the phenotype that the pathogenic genetic variant is linked to;
• ranking, using the machine learning model, the pathogenic genetic variants in the current set according to the respective importance scores;
• and
• storing the ranked pathogenic genetic variants in a variant database.