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.

The method for protein selection can include: characterizing a protein set, training a prediction model, determining target characteristic values, and determining a candidate protein set based on the target characteristic values.

The method for protein selection can include: characterizing a protein set, training a prediction model, determining target characteristic values, and determining a candidate protein set based on the target characteristic values.

The present invention relates to methods and compositions for monitoring, diagnosis, prognosis, and determination of treatment regimens in subjects suffering from or suspected of having a renal injury. In particular, the invention relates to using a measured urine concentration of one or more of TIMP2 and IGFBP7 in combination with one or more of a measured serum creatinine and a measured urine output, which results are correlated to the renal status of the subject, and can be used for diagnosis, prognosis, risk stratification, staging, monitoring, categorizing and determination of further diagnosis and treatment regimens in subjects suffering or at risk of suffering from an injury to renal function, reduced renal function, and/or acute renal failure.

The present invention relates to methods and compositions for monitoring, diagnosis, prognosis, and determination of treatment regimens in subjects suffering from or suspected of having a renal injury. In particular, the invention relates to using a measured urine concentration of one or more of TIMP2 and IGFBP7 in combination with one or more of a measured serum creatinine and a measured urine output, which results are correlated to the renal status of the subject, and can be used for diagnosis, prognosis, risk stratification, staging, monitoring, categorizing and determination of further diagnosis and treatment regimens in subjects suffering or at risk of suffering from an injury to renal function, reduced renal function, and/or acute renal failure.

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

Methods for improving clinical diagnostic tests are provided, along with associated diagnostic techniques.

Methods for improving clinical diagnostic tests are provided, along with associated diagnostic techniques.

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.