Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

Methods and systems for improving computer efficiency by intelligently selecting subsets of possible short tandem repeat (STR) allele combinations for further deconvolution analysis are disclosed. In one embodiment, at each locus, for a currently analyzed contribution ratio scenario of a plurality of contribution ratio scenarios, a processor computes an adjusted evidence profile. For a first, or next, unidentified contributor having a pre-determined highest remaining contribution ratio in the currently analyzed contribution ratio scenario for the plurality of contributors, a processor computes a first range of expected peak heights using at least the pre-determined highest remaining contribution ratio, a selected degradation value, and a peak height ratio distribution. Also disclosed are methods and systems for intelligently estimating the number of contributors to a biological sample.

The technology disclosed relates to inter-model prediction score recalibration. In one implementation, the technology disclosed relates to a system including a first model that generates, based on evolutionary conservation summary statistics of amino acids in a target protein sequence, a first pathogenicity score-to-rank mapping for a set of variants in the target protein sequence; and a second model that generates, based on epistasis expressed by amino acid patterns spanning the target protein sequence and a plurality of non-target protein sequences aligned in multiple sequence alignment, a second pathogenicity score-to-rank mapping for the set of variants. The system also includes a reassignment logic that reassigns pathogenicity scores from the first set of pathogenicity scores to the set of variants based on the first and second score-to-rank mappings, and an output logic to generate a ranking of the set of variants based on the reassigned scores.

The technology disclosed relates to inter-model prediction score recalibration. In one implementation, the technology disclosed relates to a system including a first model that generates, based on evolutionary conservation summary statistics of amino acids in a target protein sequence, a first pathogenicity score-to-rank mapping for a set of variants in the target protein sequence; and a second model that generates, based on epistasis expressed by amino acid patterns spanning the target protein sequence and a plurality of non-target protein sequences aligned in multiple sequence alignment, a second pathogenicity score-to-rank mapping for the set of variants. The system also includes a reassignment logic that reassigns pathogenicity scores from the first set of pathogenicity scores to the set of variants based on the first and second score-to-rank mappings, and an output logic to generate a ranking of the set of variants based on the reassigned scores.

Provided herein are methods, processes and apparatuses for non-invasive assessment of genetic variations.

A computer of a microbial community analysis system includes an input unit configured to input a plurality of data groups including information indicating a nucleotide sequence of a gene of each of a plurality of microorganisms included in activated sludge in which a water treatment is performed; a similarity calculating unit configured to calculate a similarity between data groups on the basis of the nucleotide sequences included in the input data groups, and a coordinates calculating unit configured to calculate coordinates in a multidimensional space of each of the data groups on the basis of the calculated similarity.

A computer of a microbial community analysis system includes an input unit configured to input a plurality of data groups including information indicating a nucleotide sequence of a gene of each of a plurality of microorganisms included in activated sludge in which a water treatment is performed; a similarity calculating unit configured to calculate a similarity between data groups on the basis of the nucleotide sequences included in the input data groups, and a coordinates calculating unit configured to calculate coordinates in a multidimensional space of each of the data groups on the basis of the calculated similarity.

Systems and methods are provided for performing predictive assignments pertaining to genetic information. One embodiment is a system that includes a genetic prediction server. The genetic prediction server includes an interface that acquires records that each indicate one or more genetic variants determined to exist within an individual, and a controller. The controller selects one or more machine learning models that utilize the genetic variants as input, and loads the machine learning models. For each individual in the records: the controller predictively assigns at least one characteristic to that individual by operating the machine learning models based on at least one genetic variant indicated in the records for that individual. The controller also generates a report indicating at least one predictively assigned characteristic for at least one individual, and transmits a command via the interface for presenting the report at a display.

Systems and methods are provided for performing predictive assignments pertaining to genetic information. One embodiment is a system that includes a genetic prediction server. The genetic prediction server includes an interface that acquires records that each indicate one or more genetic variants determined to exist within an individual, and a controller. The controller selects one or more machine learning models that utilize the genetic variants as input, and loads the machine learning models. For each individual in the records: the controller predictively assigns at least one characteristic to that individual by operating the machine learning models based on at least one genetic variant indicated in the records for that individual. The controller also generates a report indicating at least one predictively assigned characteristic for at least one individual, and transmits a command via the interface for presenting the report at a display.