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.

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.

Abstract: The present disclosure provides methods for automatically predicting the functional significance and clinical interpretation of variants (e.g., protein missense variants such as mutations) of unknown significance observed, e.g., in medical genetic testing, using the conformational dynamics of molecular structures (e.g., protein structures). The disclosure provides computer implemented methods, and integrated data, infrastructure, and software systems that can generate conformational dynamics (e.g., using molecular dynamics) of protein structures, compute features from these simulations, extract conformational states, initiate simulations for relevant variants (e.g., missense variants), and train, test, and deploy machine learning models for scoring the clinical significance of the variants.

Abstract: 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.

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.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed herein are system, method, and computer program product embodiments for determining phenotypic impacts of molecular variants identified within a biological sample. Embodiments include receiving molecular variants associated with functional elements within a model system. The embodiments then determine molecular scores associated with the model system. The embodiments then determine molecular signals and population signals associated with the molecular variants based on the molecular scores. The embodiments then determine functional scores for the molecular variants based on statistical learning. The embodiments then derive evidence scores of the molecular variants based on the functional scores. The embodiments then determine phenotypic impacts of the molecular variants based on the functional scores or evidence scores.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed herein are system, method, and computer program product embodiments for determining phenotypic impacts of molecular variants identified within a biological sample. Embodiments include receiving molecular variants associated with functional elements within a model system. The embodiments then determine molecular scores associated with the model system. The embodiments then determine molecular signals and population signals associated with the molecular variants based on the molecular scores. The embodiments then determine functional scores for the molecular variants based on statistical learning. The embodiments then derive evidence scores of the molecular variants based on the functional scores. The embodiments then determine phenotypic impacts of the molecular variants based on the functional scores or evidence scores.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed herein are system, method, and computer program product embodiments for determining phenotypic impacts of molecular variants identified within a biological sample. Embodiments include receiving molecular variants associated with functional elements within a model system. The embodiments then determine molecular scores associated with the model system. The embodiments then determine molecular signals and population signals associated with the molecular variants based on the molecular scores. The embodiments then determine functional scores for the molecular variants based on statistical learning. The embodiments then derive evidence scores of the molecular variants based on the functional scores. The embodiments then determine phenotypic impacts of the molecular variants based on the functional scores or evidence scores.

Abstract: 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.