# US 12,191,001 B2 · Population frequency modeling for quantitative variant pathogenicity estimation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,191,001 B2 - Document ID: US12191001B2 - Status: Granted - Kind: B2 - Application number: 18/700,257 - Filed: 2023-10-31 - Published: 2024-10-10 - Granted: 2025-01-07 - Earliest claimed: 2022-11-01 - Assignees: Laboratory Corporation of America Holdings - Applicants: Laboratory Corporation of America Holdings - Inventors: Toby Manders; Keith Nykamp; Alexandre Colavin; Yuya Kobayashi - Web page: https://patentgrep.com/patent/US12191001B2 ## CPC - G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding - G06N 20/00: Machine learning - G16B 20/20: Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection - G16B 20/40: Population genetics; Linkage disequilibrium - G16B 40/20: Supervised data analysis - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## 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. ## Claims ### Claim 1 A method for configuring a machine learning model to model population frequency for variant classification, the method comprising: applying a logistic regression model to a first set of population data for a first set of genes, wherein an item of the first set of population data comprises, for a variant located at a position within a gene of the first set of genes, a set of features comprising at least one gene-level feature, at least one variant-level feature, and at least one population frequency meta-feature, and a reference label that indicates whether the variant is benign or pathogenic, wherein the at least one population frequency meta-feature quantifies predictive value of allele frequency in the gene, wherein the applying comprises computing a gene-level constraint; including the gene-level constraint in the at least one gene-level feature; computing an allele frequency; including the allele frequency in the at least one variant-level feature; including, in the at least one population frequency meta-feature, a mathematical combination of the gene-level constraint and the allele frequency; and applying the logistic regression model to the set of features including the mathematical combination of the gene-level constraint and the allele frequency; wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy the at least one second performance criterion based on the set of features including the mathematical combination of the gene-level constraint and the allele frequency; for each item of the first set of population data, evaluating a variant classification prediction output by the logistic regression model based on an expected variant classification indicated by the reference label; and iteratively adjusting a value of at least one parameter or coefficient of the logistic regression model until output of a loss function computed based on the variant classification prediction output by the logistic regression model satisfies at least one first performance criterion, to produce a trained logistic regression model, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy at least one second performance criterion. ### Claim 2 (depends on 1) The method of claim 1, further comprising: using the trained logistic regression model to generate a prediction as to whether the variant is benign or pathogenic. ### Claim 3 (depends on 2) The method of claim 2, further comprising: providing the prediction as to whether the variant is benign or pathogenic to a clinician for use in formulating, by the clinician, a diagnosis of a patient. ### Claim 4 (depends on 1) The method of claim 1, further comprising: applying the trained logistic regression model to a second set of population data for a plurality of variants of a second plurality of genes; and for each variant of the second plurality of genes, receiving a variant classification prediction output by the trained logistic regression model and, in response to the variant classification prediction satisfying at least the second performance criterion, storing the variant classification prediction in association with the variant for retrieval via at least one query. ### Claim 5 (depends on 1) The method of claim 1, further comprising: computing the allele frequency by calculating a binomial proportion of a confidence value associated with the allele frequency. ### Claim 6 (depends on 1) The method of claim 1, further comprising: computing the mathematical combination of the gene-level constraint and the allele frequency by dividing the allele frequency by the gene-level constraint to produce a quotient and taking an exponent of the quotient. ### Claim 7 A method for configuring a machine learning model to model population frequency for variant classification, the method comprising: applying a logistic regression model to a first set of population data for a first set of genes, wherein an item of the first set of population data comprises, for a variant located at a position within a gene of the first set of genes, a set of features comprising at least one gene-level feature, at least one variant-level feature, and at least one population frequency meta-feature, and a reference label that indicates whether the variant is benign or pathogenic, wherein the at least one population frequency meta-feature quantifies predictive value of allele frequency in the gene; for each item of the first set of population data, evaluating a variant classification prediction output by the logistic regression model based on an expected variant classification indicated by the reference label; iteratively adjusting a value of at least one parameter or coefficient of the logistic regression model until output of a loss function computed based on the variant classification prediction output by the logistic regression model satisfies at least one first performance criterion, to produce a trained logistic regression model, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy at least one second performance criterion; computing, for the gene, an exponent of a ratio of synonymous missense variants to nonsynonymous missense variants; including the exponent of the ratio of synonymous missense variants to nonsynonymous missense variants in the at least one population frequency meta-feature; and applying the logistic regression model to the set of features including the exponent of the ratio of synonymous missense variants to nonsynonymous missense variants, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy the at least one second performance criterion based on the set of features including the exponent of the ratio of synonymous missense variants to nonsynonymous missense variants. ### Claim 8 (depends on 1) The method of claim 1, further comprising: selecting, as the set of features, not more than thirty features; and applying the logistic regression model to the selected set of not more than thirty features, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy the at least one second performance criterion based on the selected set of not more than thirty features. ### Claim 9 (depends on 1) The method of claim 1, further comprising: computing a fixation index, wherein the fixation index comprises sub-population frequency data; including the fixation index in the set of features; and applying the logistic regression model to the set of features including the fixation index, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy the at least one second performance criterion based on the set of features including the fixation index. ### Claim 10 A method for configuring a machine learning model to model population frequency for variant classification, the method comprising: applying a logistic regression model to a first set of population data for a first set of genes, wherein an item of the first set of population data comprises, for a variant located at a position within a gene of the first set of genes, a set of features comprising at least one gene-level feature, at least one variant-level feature, and at least one population frequency meta-feature, and a reference label that indicates whether the variant is benign or pathogenic, wherein the at least one population frequency meta-feature quantifies predictive value of allele frequency in the gene; for each item of the first set of population data, evaluating a variant classification prediction output by the logistic regression model based on an expected variant classification indicated by the reference label; iteratively adjusting a value of at least one parameter or coefficient of the logistic regression model until output of a loss function computed based on the variant classification prediction output by the logistic regression model satisfies at least one first performance criterion, to produce a trained logistic regression model, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy at least one second performance criterion; computing, for a variant, a mathematical combination of subpopulation frequency data and population frequency data; including the mathematical combination of subpopulation frequency data and population frequency data in the at least one variant-level feature; and applying the logistic regression model to the set of features including the mathematical combination of subpopulation frequency data and population frequency data, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy the at least one second performance criterion based on the set of features including the mathematical combination of subpopulation frequency data and population frequency data. ### Claim 11 (depends on 1) The method of claim 1, wherein iteratively adjusting the value of at least one parameter of the logistic regression model comprises adjusting a C value of the logistic regression model until the output of the loss function satisfies the at least one first performance criterion, to produce the trained logistic regression model. ### Claim 12 (depends on 1) The method of claim 1, further comprising: configuring the logistic regression model to model population frequency for variant classification using L1 regularization. ### Claim 13 (depends on 1) The method of claim 1, further comprising: estimating the first performance criterion using a means square error or area under the receiver operating characteristic curve. ### Claim 14 (depends on 1) The method of claim 1, further comprising: determining the second performance criterion using at least one of decision boundary inspection, feature weight inspection, or a comparison to a benchmark variant classification. ### Claim 15 (depends on 1) The method of claim 1, further comprising: using variant classification estimates output by the trained logistic regression model as an input to a variant classification framework. ### Claim 16 (depends on 1) The method of claim 1, wherein the at least one population frequency meta-feature comprises expected frequency distributions of known benign variants and known pathogenic variants within the gene. ### Claim 17 A system, comprising: at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: applying a logistic regression model to a first set of population data for a first set of genes, wherein an item of the first set of population data comprises, for a variant located at a position within a gene of the first set of genes, a set of features comprising at least one gene-level feature, at least one variant-level feature, and at least one population frequency meta-feature, and a reference label that indicates whether the variant is benign or pathogenic, wherein the at least one population frequency meta-feature quantifies predictive value of allele frequency in the gene, wherein the applying comprises computing a gene-level constraint; including the gene-level constraint in the at least one gene-level feature; computing an allele frequency; including the allele frequency in the at least one variant-level feature; including, in the at least one population frequency meta-feature, a mathematical combination of the gene-level constraint and the allele frequency; and applying the logistic regression model to the set of features including the mathematical combination of the gene-level constraint and the allele frequency; wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy the at least one second performance criterion based on the set of features including the mathematical combination of the gene-level constraint and the allele frequency; for each item of the first set of population data, evaluating a variant classification prediction indicated by the reference label; and adjusting a value of at least one parameter or coefficient of the logistic regression model until at least one first performance criterion is satisfied to produce a trained logistic regression model, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy at least one second performance criterion. ### Claim 18 (depends on 17) The system of claim 17, wherein the at least one memory further comprises at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: using the trained logistic regression model to generate a prediction as to whether the variant is benign or pathogenic. ### Claim 19 (depends on 18) The system of claim 18, wherein the at least one memory further comprises at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: providing the prediction as to whether the variant is benign or pathogenic to a clinician for use in formulating, by the clinician, a diagnosis of a patient. ### Claim 20 (depends on 17) The system of claim 17, wherein the at least one memory further comprises at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: applying the trained logistic regression model to a second set of population data for a plurality of variants of a second plurality of genes; and for each variant of the second plurality of genes, receiving a variant classification prediction output by the trained logistic regression model and, in response to the variant classification prediction satisfying at least the second performance criterion, storing the variant classification prediction in association with the variant for retrieval via at least one query. ### Claim 21 (depends on 17) The system of claim 17, wherein the at least one memory further comprises at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: computing the allele frequency by calculating a binomial proportion of a confidence value associated with the allele frequency. ### Claim 22 (depends on 17) The system of claim 17, wherein the at least one memory further comprises at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: computing the mathematical combination of the gene-level constraint and the allele frequency by dividing the allele frequency by the gene-level constraint to produce a quotient and taking an exponent of the quotient. ### Claim 23 A system, comprising: at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: applying a logistic regression model to a first set of population data for a first set of genes, wherein an item of the first set of population data comprises, for a variant located at a position within a gene of the first set of genes, a set of features comprising at least one gene-level feature, at least one variant-level feature, and at least one population frequency meta-feature, and a reference label that indicates whether the variant is benign or pathogenic, wherein the at least one population frequency meta-feature quantifies predictive value of allele frequency in the gene; for each item of the first set of population data, evaluating a variant classification prediction indicated by the reference label; adjusting a value of at least one parameter or coefficient of the logistic regression model until at least one first performance criterion is satisfied to produce a trained logistic regression model, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy at least one second performance criterion; computing, for the gene, an exponent of a ratio of synonymous missense variants to nonsynonymous missense variants; including the exponent of the ratio of synonymous missense variants to nonsynonymous missense variants in the at least one population frequency meta-feature; and applying the logistic regression model to the set of features including the exponent of the ratio of synonymous missense variants to nonsynonymous missense variants, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy the at least one second performance criterion based on the set of features including the exponent of the ratio of synonymous missense variants to nonsynonymous missense variants. ### Claim 24 (depends on 17) The system of claim 17, wherein the at least one memory further comprises at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: selecting, as the set of features, not more than thirty features; and applying the logistic regression model to the selected set of not more than thirty features, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy the at least one second performance criterion based on the selected set of not more than thirty features. ### Claim 25 (depends on 17) The system of claim 17, wherein the at least one memory further comprises at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: computing a fixation index, wherein the fixation index comprises sub-population frequency data; including the fixation index in the set of features; and applying the logistic regression model to the set of features including the fixation index, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy the at least one second performance criterion based on the set of features including the fixation index. ### Claim 26 A system, comprising: at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: applying a logistic regression model to a first set of population data for a first set of genes, wherein an item of the first set of population data comprises, for a variant located at a position within a gene of the first set of genes, a set of features comprising at least one gene-level feature, at least one variant-level feature, and at least one population frequency meta-feature, and a reference label that indicates whether the variant is benign or pathogenic, wherein the at least one population frequency meta-feature quantifies predictive value of allele frequency in the gene; for each item of the first set of population data, evaluating a variant classification prediction output by the logistic regression model based on an expected variant classification indicated by the reference label; adjusting a value of at least one parameter or coefficient of the logistic regression model until at least one first performance criterion is satisfied to produce a trained logistic regression model, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy at least one second performance criterion; computing, for a variant, a mathematical combination of subpopulation frequency data and population frequency data; including the mathematical combination of subpopulation frequency data and population frequency data in the at least one variant-level feature; and applying the logistic regression model to the set of features including the mathematical combination of subpopulation frequency data and population frequency data, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy the at least one second performance criterion based on the set of features including the mathematical combination of subpopulation frequency data and population frequency data. ### Claim 27 (depends on 17) The system of claim 17, wherein iteratively adjusting the value of at least one parameter of the logistic regression model comprises adjusting a C value of the logistic regression model until the output of the loss function satisfies the at least one first performance criterion, to produce the trained logistic regression model. ### Claim 28 (depends on 17) The system of claim 17, wherein the at least one memory further comprises at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: configuring the logistic regression model to model population frequency for variant classification using L1 regularization. ### Claim 29 (depends on 17) The system of claim 17, wherein the at least one memory further comprises at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: estimating the first performance criterion using a means square error or area under the receiver operating characteristic curve. ### Claim 30 (depends on 17) The system of claim 17, wherein the at least one memory further comprises at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: determining the second performance criterion using at least one of decision boundary inspection, feature weight inspection, or a comparison to a benchmark variant classification. ### Claim 31 (depends on 17) The system of claim 17, wherein the at least one memory further comprises at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: using variant classification estimates output by the trained logistic regression model as an input to a variant classification framework. ### Claim 32 (depends on 17) The system of claim 17, wherein the at least one population frequency meta-feature comprises expected frequency distributions of known benign variants and known pathogenic variants within the gene. ### Claim 33 At least one non-transitory machine-readable medium comprising at least one instruction that when executed by at least one processor causes the at least one processor to perform operations comprising: applying a logistic regression model to a first set of population data for a first set of genes, wherein an item of the first set of population data comprises, for a variant located at a position within a gene of the first set of genes, a set of features comprising at least one population frequency meta-feature and a reference label that indicates whether the variant is benign or pathogenic, wherein the at least one population frequency meta-feature quantifies predictive value of allele frequency in the gene, wherein the applying comprises computing a gene-level constraint; including the gene-level constraint in the at least one gene-level feature; computing an allele frequency; including the allele frequency in the at least one variant-level feature; including, in the at least one population frequency meta-feature, a mathematical combination of the gene-level constraint and the allele frequency; and applying the logistic regression model to the set of features including the mathematical combination of the gene-level constraint and the allele frequency; wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy the at least one second performance criterion based on the set of features including the mathematical combination of the gene-level constraint and the allele frequency; for each item of the first set of population data, evaluating a variant classification prediction indicated by the reference label; and adjusting a value of at least one parameter or coefficient of the logistic regression model until at least one first performance criterion is satisfied to produce a trained logistic regression model, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy at least one second performance criterion. ### Claim 34 (depends on 33) The at least one non-transitory machine-readable medium of claim 33, further comprising at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: using the trained logistic regression model to generate a prediction as to whether the variant is benign or pathogenic. ### Claim 35 (depends on 34) The at least one non-transitory machine-readable medium of claim 34, further comprising at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: providing the prediction as to whether the variant is benign or pathogenic to a clinician for use in formulating, by the clinician, a diagnosis of a patient. ### Claim 36 (depends on 33) The at least one non-transitory machine-readable medium of claim 33, further comprising at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: applying the trained logistic regression model to a second set of population data for a plurality of variants of a second plurality of genes; and for each variant of the second plurality of genes, receiving a variant classification prediction output by the trained logistic regression model and, in response to the variant classification prediction satisfying at least the second performance criterion, storing the variant classification prediction in association with the variant for retrieval via at least one query. ### Claim 37 (depends on 33) The at least one non-transitory machine-readable medium of claim 33, further comprising at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: computing the allele frequency by calculating a binomial proportion of a confidence value associated with the allele frequency. ### Claim 38 (depends on 33) The at least one non-transitory machine-readable medium of claim 33, further comprising at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: computing the mathematical combination of the gene-level constraint and the allele frequency by dividing the allele frequency by the gene-level constraint to produce a quotient and taking an exponent of the quotient. ### Claim 39 At least one non-transitory machine-readable medium comprising at least one instruction that when executed by at least one processor causes the at least one processor to perform operations comprising: applying a logistic regression model to a first set of population data for a first set of genes, wherein an item of the first set of population data comprises, for a variant located at a position within a gene of the first set of genes, a set of features comprising at least one population frequency meta-feature and a reference label that indicates whether the variant is benign or pathogenic, wherein the at least one population frequency meta-feature quantifies predictive value of allele frequency in the gene; for each item of the first set of population data, evaluating a variant classification prediction indicated by the reference label; adjusting a value of at least one parameter or coefficient of the logistic regression model until at least one first performance criterion is satisfied to produce a trained logistic regression model, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy at least one second performance criterion; computing, for the gene, an exponent of a ratio of synonymous missense variants to nonsynonymous missense variants; including the exponent of the ratio of synonymous missense variants to nonsynonymous missense variants in the at least one population frequency meta-feature; and applying the logistic regression model to the set of features including the exponent of the ratio of synonymous missense variants to nonsynonymous missense variants, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy the at least one second performance criterion based on the set of features including the exponent of the ratio of synonymous missense variants to nonsynonymous missense variants. ### Claim 40 (depends on 33) The at least one non-transitory machine-readable medium of claim 33, further comprising at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: selecting, as the set of features, not more than thirty features; and applying the logistic regression model to the selected set of not more than thirty features, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy the at least one second performance criterion based on the selected set of not more than thirty features. ### Claim 41 (depends on 33) The at least one non-transitory machine-readable medium of claim 33 further comprising at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: including the fixation index in the set of features; and applying the logistic regression model to the set of features including the fixation index, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy the at least one second performance criterion based on the set of features including the fixation index. ### Claim 42 At least one non-transitory machine-readable medium comprising at least one instruction that when executed by at least one processor causes the at least one processor to perform operations comprising: applying a logistic regression model to a first set of population data for a first set of genes, wherein an item of the first set of population data comprises, for a variant located at a position within a gene of the first set of genes, a set of features comprising at least one population frequency meta-feature and a reference label that indicates whether the variant is benign or pathogenic, wherein the at least one population frequency meta-feature quantifies predictive value of allele frequency in the gene; for each item of the first set of population data, evaluating a variant classification prediction output by the logistic regression model based on an expected variant classification indicated by the reference label; adjusting a value of at least one parameter or coefficient of the logistic regression model until at least one first performance criterion is satisfied to produce a trained logistic regression model, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy at least one second performance criterion; computing, for a variant, a mathematical combination of subpopulation frequency data and population frequency data; and applying the logistic regression model to the set of features including the mathematical combination of subpopulation frequency data and population frequency data, wherein the trained logistic regression model is capable of outputting variant pathogenicity estimates that satisfy the at least one second performance criterion based on the set of features including the mathematical combination of subpopulation frequency data and population frequency data. ### Claim 43 (depends on 33) The at least one non-transitory machine-readable medium of claim 33, wherein adjusting the value of at least one parameter of the logistic regression model comprises adjusting a C value of the logistic regression model until output of a loss function satisfies the at least one first performance criterion, to produce the trained logistic regression model. ### Claim 44 (depends on 33) The at least one non-transitory machine-readable medium of claim 33, further comprising at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: configuring the logistic regression model to model population frequency for variant classification using L1 regularization. ### Claim 45 (depends on 33) The at least one non-transitory machine-readable medium of claim 33, further comprising at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: estimating the at least one first performance criterion using a means square error or area under the receiver operating characteristic curve. ### Claim 46 (depends on 33) The at least one non-transitory machine-readable medium of claim 33, further comprising at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: determining the second performance criterion using at least one of decision boundary inspection, feature weight inspection, or a comparison to a benchmark variant classification. ### Claim 47 (depends on 33) The at least one non-transitory machine-readable medium of claim 33, further comprising at least one instruction that when executed by the at least one processor causes the at least one processor to perform at least one operation comprising: using variant classification estimates output by the trained logistic regression model as an input to a variant classification framework. ### Claim 48 (depends on 33) The at least one non-transitory machine-readable medium of claim 33, wherein the at least one population frequency meta-feature comprises expected frequency distributions of known benign variants and known pathogenic variants within the gene.