Patents by Inventor Jeremy Francis McRAE
Jeremy Francis McRAE 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).
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Patent number: 11798650Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional neural network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.Type: GrantFiled: October 15, 2018Date of Patent: October 24, 2023Assignee: Illumina, Inc.Inventors: Laksshman Sundaram, Kai-How Farh, Hong Gao, Jeremy Francis McRae
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Publication number: 20230245717Abstract: Described herein are technologies for converting context of an ANN or context of another type of computing system that is trainable through machine learning. In some implementations, the technologies convert a first context of a computing system (such as an ANN), which is to provide pathogenicity of variants of genomes of a population, to a second context of the computing system, which is to provide pathogenicity of indels of the genomes of the population.Type: ApplicationFiled: January 27, 2023Publication date: August 3, 2023Inventors: Jeremy Francis McRae, Yanshen Yang, Marc Fasnacht, Kai-How Farh
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Publication number: 20230207132Abstract: A computer-implemented method of predicting phenotypic shift in response to usage of a plurality of drugs on a plurality of phenotypes of a cohort of individuals with a plurality of confounders. The cohort of individuals has associated phenotype measurements, covariate measurements, and drug usage patterns for two separate time points. The phenotype measurements for the first and second time points are covariate-corrected and drug-usage corrected through the use of biostatistics.Type: ApplicationFiled: October 18, 2022Publication date: June 29, 2023Applicant: ILLUMINA, INC.Inventors: Petko Plamenov FIZIEV, Jeremy Francis MCRAE, Kai-How FARH
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Publication number: 20230207067Abstract: A computer-implemented method of performing an optimized burden test for a particular gene, in which an optimal combination of a maximum allele count and a minimum pathogenicity score threshold that maximize significance of burden testing for rare deleterious variants are determined using a grid search protocol. Each combination of maximum allele count and minimum pathogenicity score threshold is tested with a t-test to obtain effect size and p-value. The combination of allele count and pathogenicity score threshold with the most significant p-value is selected as the optimal parameters for the rare deleterious variant burden test for a particular gene.Type: ApplicationFiled: October 18, 2022Publication date: June 29, 2023Applicant: ILLUMINA, INC.Inventors: Petko Plamenov FIZIEV, Jeremy Francis MCRAE, Kai-How FARH
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Publication number: 20230207052Abstract: A computer-implemented method of quantifying a strength of association of genes associated with a phenotype and a contribution of rare variants to a phenotype response by calculating a weighted burden score for a plurality of associated genes with a specified phenotype, wherein the burden score identifies identifying consequential, non-random association in a cohort between carrier status of each of the associated genes and a phenotype response to presence in the associated genes of one or more rare pathogenic variants. Respective effective strength scores are determined for the consequential, non-random association for genes selected from the associated genes based on respective burden scores at per-gene resolution.Type: ApplicationFiled: October 18, 2022Publication date: June 29, 2023Applicant: ILLUMINA, INC.Inventors: Petko Plamenov FIZIEV, Jeremy Francis MCRAE, Kai-How FARH
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Publication number: 20230059877Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional network network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.Type: ApplicationFiled: October 20, 2022Publication date: February 23, 2023Applicant: Illumina, Inc.Inventors: Kishore JAGANATHAN, Kai-How FARH, Sofia KYRIAZOPOULOU PANAGIOTOPOULOU, Jeremy Francis MCRAE
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Patent number: 11488009Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional neural network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.Type: GrantFiled: October 15, 2018Date of Patent: November 1, 2022Assignee: Illumina, Inc.Inventors: Kishore Jaganathan, Kai-How Farh, Sofia Kyriazopoulou Panagiotopoulou, Jeremy Francis McRae
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Publication number: 20220237457Abstract: The technology disclosed relates to constructing a computer-implemented method for variant classification. In particular, the method includes using a pathogenicity prediction neural network to process as input, (i) a reference protein sequence that has a first chain of amino acids with at least twenty amino acids, (ii) an alternative protein sequence aligned with the reference sequence, where the alternative protein sequence has a second chain of amino acids with at least twenty amino acids, and the first and second chains of amino acids differ by a variant amino acid caused by a nucleotide substitution, and (iii) a primate conservation profile generated using a primate cross-species multiple sequence alignment that aligns the reference protein sequence with other protein sequences from primate species. The method further includes based on the processing of the input by the neural network, generating as output a pathogenicity prediction for the nucleotide substitution.Type: ApplicationFiled: April 6, 2022Publication date: July 28, 2022Applicant: Illumina, Inc.Inventors: Laksshman Sundaram, Kai-How Farh, Hong Gao, Samskruthi Reddy Padigepati, Jeremy Francis McRae
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Patent number: 11397889Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional network network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.Type: GrantFiled: October 15, 2018Date of Patent: July 26, 2022Assignee: Illumina, Inc.Inventors: Kishore Jaganathan, Kai-How Farh, Sofia Kyriazopoulou Panagiotopoulou, Jeremy Francis McRae
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Patent number: 11386324Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional neural network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.Type: GrantFiled: January 27, 2020Date of Patent: July 12, 2022Assignee: Illumina, Inc.Inventors: Hong Gao, Kai-How Farh, Laksshman Sundaram, Jeremy Francis McRae
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Patent number: 11315016Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional neural network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.Type: GrantFiled: October 15, 2018Date of Patent: April 26, 2022Assignee: Illumina, Inc.Inventors: Laksshman Sundaram, Kai-How Farh, Hong Gao, Samskruthi Reddy Padigepati, Jeremy Francis McRae
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Publication number: 20220028485Abstract: Derivation and use of pathogenicity scores for gene variants are described herein. Applications, uses, and variations of the pathogenicity scoring process include, but are not limited to, the derivation and use of thresholds to characterize a variant as pathogenic or benign, the estimation of selection effects associated with a gene variant, the estimation of genetic disease prevalence using pathogenicity scores, and the recalibration of methods used to assess pathogenicity scores.Type: ApplicationFiled: July 21, 2021Publication date: January 27, 2022Inventors: Hong Gao, Kai-How Farh, Jeremy Francis McRae
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Publication number: 20220027388Abstract: Derivation and use of pathogenicity scores for gene variants are described herein. Applications, uses, and variations of the pathogenicity scoring process include, but are not limited to, the derivation and use of thresholds to characterize a variant as pathogenic or benign, the estimation of selection effects associated with a gene variant, the estimation of genetic disease prevalence using pathogenicity scores, and the recalibration of methods used to assess pathogenicity scores.Type: ApplicationFiled: July 21, 2021Publication date: January 27, 2022Inventors: Hong Gao, Kai-How Farh, Jeremy Francis McRae
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Publication number: 20200279157Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional neural network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.Type: ApplicationFiled: January 27, 2020Publication date: September 3, 2020Applicant: Illumina, Inc.Inventors: Hong GAO, Kai-How FARH, Laksshman SUNDARAM, Jeremy Francis McRAE
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Publication number: 20200065675Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional network network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.Type: ApplicationFiled: October 15, 2018Publication date: February 27, 2020Applicant: Illumina, Inc.Inventors: Laksshman Sundaram, Kai-How Farh, Hong Gao, Samskruthi Reddy Padigepati, Jeremy Francis McRae
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Patent number: 10558915Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional neutral network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.Type: GrantFiled: May 15, 2019Date of Patent: February 11, 2020Assignee: Illumina, Inc.Inventors: Hong Gao, Kai-How Farh, Laksshman Sundaram, Jeremy Francis McRae
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Patent number: 10423861Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional network network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.Type: GrantFiled: October 15, 2018Date of Patent: September 24, 2019Assignee: Illumina, Inc.Inventors: Hong Gao, Kai-How Farh, Laksshman Sundaram, Jeremy Francis McRae
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Publication number: 20190266491Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional network network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.Type: ApplicationFiled: May 15, 2019Publication date: August 29, 2019Applicant: Illumina, Inc.Inventors: Hong GAO, Kai-How FARH, Laksshman SUNDARAM, Jeremy Francis McRAE
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Publication number: 20190197401Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional network network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.Type: ApplicationFiled: October 15, 2018Publication date: June 27, 2019Applicant: Illumina, Inc.Inventors: Kishore JAGANATHAN, Kai-How FARH, Sofia KYRIAZOPOULOU PANAGIOTOPOULOU, Jeremy Francis McRAE
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Publication number: 20190114511Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional network network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.Type: ApplicationFiled: October 15, 2018Publication date: April 18, 2019Applicant: Illumina, Inc.Inventors: Hong GAO, Kai-How FARH, Laksshman SUNDARAM, Jeremy Francis McRAE