A screening system includes control circuitry that determines gene variants present in a compiled genome representative of a subject based on a difference between a reference genome and the compiled genome representative of the subject, and acquires phenotype information from an observation of the subject. The control circuitry further generates multi-dimensional data structure that includes the gene variants in respect of a first dimension, the phenotype information in respect of a second dimension; and a set of data samples in respect of a third dimension. The set of data samples includes the compiled genome sequence representative of the subject, and corresponding historical data samples of other subjects including their corresponding transcript information (for example, including phenotype information) of the other subjects and their gene variants. The control circuitry executes a gene variant interpretation using a correlation function to find phenotype-gene variant relationships based on the generated multi-dimensional data structure.

A screening system includes control circuitry that determines gene variants present in a compiled genome representative of a subject based on a difference between a reference genome and the compiled genome representative of the subject, and acquires phenotype information from an observation of the subject. The control circuitry further generates multi-dimensional data structure that includes the gene variants in respect of a first dimension, the phenotype information in respect of a second dimension; and a set of data samples in respect of a third dimension. The set of data samples includes the compiled genome sequence representative of the subject, and corresponding historical data samples of other subjects including their corresponding transcript information (for example, including phenotype information) of the other subjects and their gene variants. The control circuitry executes a gene variant interpretation using a correlation function to find phenotype-gene variant relationships based on the generated multi-dimensional data structure.

A platform providing methods and systems for prevention and/or treatment of a health condition, where a method can include: simultaneously reducing severity symptoms of the health condition and comorbid conditions upon: receiving a set of samples from one or more subjects; receiving a biometric dataset from one or more subjects; receiving a lifestyle dataset from one or more subjects; returning a genomic single nucleotide polymorphism (SNP) profile and a baseline microbiome state upon processing the set of samples, the biometric dataset, and the lifestyle dataset with a set of transformation operations; generating personalized intervention plans for the one or more subjects upon processing the genomic SNP profile and the baseline microbiome state with a multi-omic model; and executing the personalized intervention plans for the one or more subjects.

A platform providing methods and systems for prevention and/or treatment of a health condition, where a method can include: simultaneously reducing severity symptoms of the health condition and comorbid conditions upon: receiving a set of samples from one or more subjects; receiving a biometric dataset from one or more subjects; receiving a lifestyle dataset from one or more subjects; returning a genomic single nucleotide polymorphism (SNP) profile and a baseline microbiome state upon processing the set of samples, the biometric dataset, and the lifestyle dataset with a set of transformation operations; generating personalized intervention plans for the one or more subjects upon processing the genomic SNP profile and the baseline microbiome state with a multi-omic model; and executing the personalized intervention plans for the one or more subjects.

The technology disclosed describes determination of which elements of a sequence are nearest to uniformly spaced cells in a grid, where the elements have element coordinates, and the cells have dimension-wise cell indices and cell coordinates. The determination includes generating an element-to-cells mapping that maps, to each of the elements, a subset of the cells. The subset of the cells mapped to a particular element in the sequence includes a nearest cell in the grid and one or more neighborhood cells in the grid, and the nearest cell is selected based on matching element coordinates of the particular element to the cell coordinates. The determination further includes generating a cell-to-elements mapping that maps, to each of the cells, a subset of the elements, and using the cell-to-elements mapping to determine, for each of the cells, a nearest element in the sequence.

The technology disclosed describes determination of which elements of a sequence are nearest to uniformly spaced cells in a grid, where the elements have element coordinates, and the cells have dimension-wise cell indices and cell coordinates. The determination includes generating an element-to-cells mapping that maps, to each of the elements, a subset of the cells. The subset of the cells mapped to a particular element in the sequence includes a nearest cell in the grid and one or more neighborhood cells in the grid, and the nearest cell is selected based on matching element coordinates of the particular element to the cell coordinates. The determination further includes generating a cell-to-elements mapping that maps, to each of the cells, a subset of the elements, and using the cell-to-elements mapping to determine, for each of the cells, a nearest element in the sequence.

A system and method are presented in which known genetic attributes associated with a condition are used to seed the determination of additional attributes which are associated with the condition. Based on the learning, the additional attributes (genetic, behavioral, or both) provide for an increased correlation between the combined attributes and the condition. For behavioral attributes, a measure of the impact of the behavioral attribute on the risk of the condition can be transmitted to another device or system.

A system and method are presented in which known genetic attributes associated with a condition are used to seed the determination of additional attributes which are associated with the condition. Based on the learning, the additional attributes (genetic, behavioral, or both) provide for an increased correlation between the combined attributes and the condition. For behavioral attributes, a measure of the impact of the behavioral attribute on the risk of the condition can be transmitted to another device or system.

A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.

A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.