Abstract: A system includes a computing device having 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 including receiving a first named entity dataset associated with a first named entity, receiving a second named entity dataset associated with a second named entity who is a potential named entity linked to the first named entity, identifying a plurality of data matches, each data match being a named entity whose data segments match the first named entity dataset, the second named entity dataset, or both, extracting features from the plurality of data matches, and inputting the extracted features into a machine learning model to determine a data-origin link between the first named entity and the second named entity.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating a matched-data-link tree defining relationships among individuals according to genetic data. For example, the disclosed systems can determine matches of matches using a novel matching database structure. The disclosed systems encode integers based on data matches of a data identifier and populate a match database with the integers. Correlating data match integers for a data identifier with data match integers for data matches, the disclosed systems generate matches of matches. The disclosed systems can generate relationship clusters from matches of matches. For example, the disclosed systems compare sets of data matches to discover groups of data matches with stronger data-match levels. The disclosed systems can generate and populate a data-link tree from a relationship cluster. In addition, the disclosed systems can merge one or more data-link trees into a single data-link tree.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating a matched-data-link tree defining relationships among individuals according to genetic data. For example, the disclosed systems can determine matches of matches using a novel matching database structure. The disclosed systems encode integers based on data matches of a data identifier and populate a match database with the integers. Correlating data match integers for a data identifier with data match integers for data matches, the disclosed systems generate matches of matches. The disclosed systems can generate relationship clusters from matches of matches. For example, the disclosed systems compare sets of data matches to discover groups of data matches with stronger data-match levels.

Abstract: Disclosed is a method for determining inheritance labels of users based on inheritance datasets of the users. The method includes generating a plurality of reference panels for a plurality of data-inheritance origins, each reference panel corresponding to a data-inheritance origin and comprising reference-panel datasets representative of the data-inheritance origin. The method constructs a plurality of simulated data trees that are built using the reference-panel datasets that are selected from the plurality of reference panels. The method generates a plurality of simulated inheritance datasets representing a plurality of simulated named entities, each representing a descendant named entity in one of the simulated data trees. The method trains a machine learning model to determine inheritance labels of an inheritance dataset.

Abstract: A computing device may receive an inheritance dataset of a target domestic companion animal that belongs to a first owner, the first owner being a user of an online system. The computing device accesses inheritance datasets of reference panel animals. The reference panel animals are organized into breeds. The computing device compares the inheritance dataset of the target domestic companion animal to the inheritance datasets of the reference panel animals to identify breeds of the target domestic companion animal. The computing device identifies a plurality of matched domestic companion animals in the breeds of the target domestic companion animal. The computing device filters the matched domestic companion animals based on geographical proximity. The computing device causes to display a filtered matched domestic companion animal to the first owner of the target domestic companion animal to indicate potential social matches for the target domestic companion animal.

Abstract: A computing device may receive an inheritance dataset of a target named entity. The device may access a plurality of clusters associated with a region, each cluster comprising inheritance data for a plurality of reference panel named entities. The device may determine that the inheritance dataset of the target named entity has at least a threshold amount of inheritance sequences that are classified to the region. The device may compare, for each cluster, the inheritance dataset of the target named entity to the reference panel named entities in the cluster to identify similarities and shared inheritance segments between the target named entity and the reference panel named entities. The device may determine, for each cluster, a metric based on the inheritance segments shared. The device may assign the target named entity to one or more ethnicities based on the comparison between the metric and the threshold specific to the cluster.

Abstract: Disclosed are methods and system for predicting data-source influences on one or more data manifestations of a named entity. The method includes receiving an inheritance dataset of the named entity. The method determines first and second portions of the inheritance dataset of the named entity. The method determines an aggregated data-bit association score for the named entity based on the inheritance dataset at an identified subset of the data-bit regions. The method determines aggregated data-bit association scores associated with the first and second data source based on the first and second portions of the inheritance dataset at the identified subset of the data-bit regions. The method selects one of the first and second data sources as having a measure of influence on a data manifestation of the named entity corresponding to the identified subset of the data-bit regions.

Abstract: A user of a genetic database may create and build upon their family tree in the database. For new users, creating a family tree can be difficult and time consuming. Even for users with established family trees, extending their family tree is a challenge requiring extensive research. Disclosed herein are embodiments for assisting users of a genetic database with building their family trees. In some embodiments, a method for assisting with constructing family trees includes receiving a target individual's genetic dataset. The method identifies a plurality of matched individuals who genetically match the target individual. The method identifies potential ancestors who are potential common ancestors between the target individual and one of the matched individuals. The method inputs a set of features related to the target individual to a machine learning model and filters the potential common ancestors to determine a subset of likely common ancestors for the target individual.

Abstract: A computing device may receive an inheritance dataset of a target named entity. The device may access a plurality of clusters associated with a region, each cluster comprising inheritance data for a plurality of reference panel named entities. The device may determine that the inheritance dataset of the target named entity has at least a threshold amount of inheritance sequences that are classified to the region. The device may compare, for each cluster, the inheritance dataset of the target named entity to the reference panel named entities in the cluster to identify similarities and shared inheritance segments between the target named entity and the reference panel named entities. The device may determine, for each cluster, a metric based on the inheritance segments shared. The device may assign the target named entity to one or more ethnicities based on the comparison between the metric and the threshold specific to the cluster.

Abstract: A system includes a computing device having 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 including receiving a first named entity dataset associated with a first named entity, receiving a second named entity dataset associated with a second named entity who is a potential named entity linked to the first named entity, identifying a plurality of data matches, each data match being a named entity whose data segments match the first named entity dataset, the second named entity dataset, or both, extracting features from the plurality of data matches, and inputting the extracted features into a machine learning model to determine a data-origin link between the first named entity and the second named entity.

Abstract: Disclosed are methods for predicting assignments of users to groups based on obtained data. The methods may include phasing the obtained data of a user and inputting features of the phased data to a trained model to receive an output that provides information associated with a group assignment of the user. The model may be trained with a plurality of training samples, each training sample comprising features of phased user data of a reference user and a label identifying whether the reference user belongs to a group. Training the model may include applying a plurality of classifiers to at least one of the training samples to obtain an output from each classifier, determining an average of the outputs from applying the plurality of classifiers, and determining the average of the plurality of classifiers as the output from the model.

Abstract: Disclosed are methods and system for predicting data-source influences on one or more data manifestations of a named entity. The method includes receiving an inheritance dataset of the named entity. The method determines first and second portions of the inheritance dataset of the named entity. The method determines an aggregated data-bit association score for the named entity based on the inheritance dataset at an identified subset of the data-bit regions. The method determines aggregated data-bit association scores associated with the first and second data source based on the first and second portions of the inheritance dataset at the identified subset of the data-bit regions. The method selects one of the first and second data sources as having a measure of influence on a data manifestation of the named entity corresponding to the identified subset of the data-bit regions.

Abstract: A computing device may receive an inheritance dataset of a target named entity. The device may access a plurality of clusters associated with a region, each cluster comprising inheritance data for a plurality of reference panel named entities. The device may determine that the inheritance dataset of the target named entity has at least a threshold amount of inheritance sequences that are classified to the region. The device may compare, for each cluster, the inheritance dataset of the target named entity to the reference panel named entities in the cluster to identify similarities and shared inheritance segments between the target named entity and the reference panel named entities. The device may determine, for each cluster, a metric based on the inheritance segments shared. The device may assign the target named entity to one or more ethnicities based on the comparison between the metric and the threshold specific to the cluster.

Abstract: A user of a genetic database may create and build upon their family tree in the database. For new users, creating a family tree can be difficult and time consuming. Even for users with established family trees, extending their family tree is a challenge requiring extensive research. Disclosed herein are embodiments for assisting users of a genetic database with building their family trees. In some embodiments, a method for assisting with constructing family trees includes receiving a target individual's genetic dataset. The method identifies a plurality of matched individuals who genetically match the target individual. The method identifies potential ancestors who are potential common ancestors between the target individual and one of the matched individuals. The method inputs a set of features related to the target individual to a machine learning model and filters the potential common ancestors to determine a subset of likely common ancestors for the target individual.

Abstract: Disclosed herein relates to a method that improves the prediction of relationships between individuals. Relationship prediction systems, methods, and computer-program products are described. Relationship prediction of a most recent common ancestor and a most likely relative is performed using a multilabel-multiclass classification based on k-nearest neighbors classification, logistic regression, and/or other classification approaches. Predicting a most recent common ancestor narrows the number of possible relationships between a user of a genealogical research service and a relative and facilitates more intuitive discoveries and more specific identification of a most likely relationship.

Abstract: A method performed by one or more processors, comprising: receiving genomic data and trait data representative of a plurality of traits of one or more individuals; determining a structure of one or more of the genomic data and the trait data; selecting, in response to the determined structure, a structured association algorithm for execution with the genomic data and the trait data; generating, based on execution of the selected, structured association algorithm against the genomic data and the trait data, structured association data indicative of associations among the genomic data and the trait data, wherein the associations are at least partly identified based on the structure; and generating data for a graphical user interface, that when rendered on a display device, comprises: a visual representation of at least a portion of the structured association data.