Patents by Inventor Keith Nykamp

Keith Nykamp has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • POPULATION FREQUENCY MODELING FOR QUANTITATIVE VARIANT PATHOGENICITY ESTIMATION
    Publication number: 20250069702
    Abstract: 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.
    Type: Application
    Filed: November 11, 2024
    Publication date: February 27, 2025
    Inventors: Toby Manders, Keith Nykamp, Alexandre Colavin, Yuya Kobayashi
  • Population frequency modeling for quantitative variant pathogenicity estimation
    Patent number: 12191001
    Abstract: 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.
    Type: Grant
    Filed: October 31, 2023
    Date of Patent: January 7, 2025
    Assignee: Laboratory Corporation of America Holdings
    Inventors: Toby Manders, Keith Nykamp, Alexandre Colavin, Yuya Kobayashi
  • POPULATION FREQUENCY MODELING FOR QUANTITATIVE VARIANT PATHOGENICITY ESTIMATION
    Publication number: 20240339177
    Abstract: 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.
    Type: Application
    Filed: October 31, 2023
    Publication date: October 10, 2024
    Inventors: Toby Manders, Keith Nykamp, Alexandre Colavin, Yuya Kobayashi