Laboratory Corporation of America Holdings

Invitae Corporation

Leland Stanford Junior University

Alexandre Colavin

Carlos L Araya

Jason A Reuter

Yuya Kobayashi

Toby Manders

Kerwyn Casey Huang

Keith Nykamp

Embodiments of the disclosed technologies apply a logistic regression model to a set of population data for a set of genes. The set of population data includes a set of features for a variant located at a position within a gene. The set of features includes at least one population frequency meta-feature. The at least one population frequency meta-feature quantifies predictive value of allele frequency in the gene. Using the first set of population data, a variant classification prediction output by the logistic regression model is evaluated based on an expected variant classification. The logistic regression model is adjusted until at least one first performance criterion is satisfied to produce a trained logistic regression model. The trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy at least one second performance criterion.

Population frequency modeling for quantitative variant pathogenicity estimation

Disclosed herein are system, method, and computer program product embodiments for optimizing the determination of a phenotypic impact of a molecular variant identified in molecular tests, samples, or reports of subjects by way of regularly incorporating, updating, monitoring, validating, selecting, and auditing the best-performing evidence models for the interpretation of molecular variants across a plurality of evidence classes.

Molecular evidence platform for auditable, continuous optimization of variant interpretation in genetic and genomic testing and analysis

Generation of biomolecule sequence coevolution data structures, matrices, scores, and sectors are described. Generally, the generated coevolution data removes covariant noise due to phylogenetic drift and can reveal coevolution of residue positions in multiple phylogenetic distances. Scores can be built upon the data structures and matrices to reveal sectors of residue positions that function and evolve together. Furthermore, the coevolution data structures, matrices, scores, and sectors can be used to predict structure or function of residue variants.

Methods and systems for identification of biomolecule sequence coevolution and applications thereof

Growing community of inventors

Stanford, CA, United States of America

Alexandre Colavin