Determining relative relationships of people who share a common ancestor within at least a threshold number of generations includes: receiving recombinable deoxyribonucleic acid (DNA) sequence information of a first user and recombinable DNA sequence information of a plurality of users; processing, using one or more computer processors, the recombinable DNA sequence information of the plurality of users in parallel; determining, based at least in part on a result of processing the recombinable DNA information of the plurality of users in parallel, a predicted degree of relationship between the first user and a user among the plurality of users, the predicted degree of relative relationship corresponding to a number of generations within which the first user and the second user share a common ancestor.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing genomic predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform genomic predictive data analysis operations by using at least one of viral genomic processing machine learning models and bacterial genomic processing machine learning models.

The technology disclosed relates to a system for inter-model prediction score recalibration. The system includes a first model that generates, based on evolutionary conservation summary statistics of amino acids in a reference protein sequence, a first set of pathogenicity scores with rankings for variants that mutate the reference sequence to alternate protein sequences. The system further includes a second model that generates, based on epistasis expressed by amino acid patterns spanning a multiple sequence alignment aligning the reference sequence to non-target sequences, a second set of pathogenicity scores with rankings for the variants. The system further includes a rank loss determination logic that determines a rank loss parameter by comparing the two sets of rankings, a loss function reconfiguration logic that reconfigures a loss function based on the rank loss parameter, and a training logic that uses the reconfigured loss function to train the first model.

Introduced here is an approach for managing errors generated during artificial intelligence-based analysis encrypted data. As an illustrative example, a computing system can may be configured to generate, train, and/or implement machine learning (ML) models to detect or predict aspects of one or more types of cancer based on homomorphically encrypted patient health data. The computing system may selectively identify timing for implementing a noise management mechanism during the data processing for the ML models.

A system and method for predicting gut health of an individual using non-invasive technique has been provided. The system is making use of two types of pathways i.e. one which are beneficial to gut health and the second which are harmful to gut health. These two types of pathways are annotated in the genomes of gut bacteria. Best combinations of subsets of these pathways capable of distinguishing between gut commensals and pathogens are assigned as pathway biomarkers. The identified pathway biomarkers are then used to develop scheme for prediction of gut health status.

A system and method for predicting gut health of an individual using non-invasive technique has been provided. The system is making use of two types of pathways i.e. one which are beneficial to gut health and the second which are harmful to gut health. These two types of pathways are annotated in the genomes of gut bacteria. Best combinations of subsets of these pathways capable of distinguishing between gut commensals and pathogens are assigned as pathway biomarkers. The identified pathway biomarkers are then used to develop scheme for prediction of gut health status.

A computer-implemented method comprises: storing a trained machine learning model, the machine learning model comprising a predictor sub-model and a smoothing sub-model, the machine learning model being trained based on segments of training genomic sequences that have known ancestral origins; receiving data representing an input genomic sequence of the subject, the input genomic sequence covering a plurality of segments including a plurality of single nucleotide polymorplasms (SNP) sites of the genome of the subject, wherein each segment comprises a sequence of SNP values at the SNP sites, each SNP value specifying a variant at the SNP site; determining, using the predictor sub-model and based on the data, an initial ancestral origin estimate of each segment of SNP values; and performing, by the smoothing sub-model for each segment, a smoothing operation over the initial ancestral origin estimates to obtain a final prediction result for the ancestral origin of the segment.

Technology provided herein relates in part to methods, processes, machines and apparatuses for non-invasive assessment of genomic nucleic acid instability and genomic nucleic acid stability.

The disclosed embodiments concern methods, apparatus, systems and computer program products for determining and displaying pedigrees based on IBD data. Some implementations use a probabilistic relationship model to obtain various likelihoods of various potential relationships based on pairwise IBD data and pairwise age data. Some implementations build large pedigrees by combining smaller pedigrees. Some implementations display pedigree graphs with various features that are informative and easy to understand.

Determining relative relationships of people who share a common ancestor within at least a threshold number of generations includes: receiving recombinable deoxyribonucleic acid (DNA) sequence information of a first user and recombinable DNA sequence information of a plurality of users; processing, using one or more computer processors, the recombinable DNA sequence information of the plurality of users in parallel; determining, based at least in part on a result of processing the recombinable DNA information of the plurality of users in parallel, a predicted degree of relationship between the first user and a user among the plurality of users, the predicted degree of relative relationship corresponding to a number of generations within which the first user and the second user share a common ancestor.