Gene-specific prediction
AssigneeUniversity of Utah Research Foundation
InventorsDavid K. Crockett, Perry G. Ridge
Life of this patent
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Abstract
A gene-specific prediction tool for classifying and interpreting gene tests is described. The prediction tool includes a classifier trained and tested using databases of gene variants and their known phenotypes. The classifier uses differences between features of amino acids in obtaining attributes used to perform classification and generate predictions, including for benign and pathologic outcomes, for uncertain gene variants.
Claims16
A method of predicting a result of genetic mutation, comprising:
- determining differences between (i) values of a first plurality of features, of amino acids encoded by a variant of a wild-type polynucleotide sequence, and (ii) values of a second plurality of features, of amino acids encoded by the wild-type polynucleotide sequence;
- wherein the features of the first plurality are the same as the features of the second plurality;
- and
- by a processor and based on the differences, determining a predicted phenotype severity of the variant.
A method of predicting a result of genetic mutation, comprising:
- determining differences between (i) values of a first plurality of features, of amino acids encoded by a variant of a wild-type polynucleotide sequence, and (ii) values of a second plurality of features, of amino acids encoded by the wild-type polynucleotide sequence;
- wherein the features of the first plurality are the same as the features of the second plurality;
- by a processor, training a machine learning classification algorithm using the differences;
- and
- determining, by the trained algorithm, a prediction of phenotype severity of the gene variant.
A non-transitory computer-readable medium encoded with machine-executable instructions for:
- determining differences between (i) values of a first plurality of features, of amino acids encoded by a variant of a wild-type polynucleotide sequence, and (ii) values of a second plurality of features, of amino acids encoded by the wild-type polynucleotide sequence;
- wherein the features of the first plurality are the same as the features of the second plurality;
- and
- by a processor and based on the differences, determining a predicted phenotype severity of the variant.
A non-transitory computer-readable medium encoded with machine-executable instructions for:
- determining differences between (I) values of a first plurality of features, of amino acids encoded by a variant of a wild-type polynucleotide sequence, and (ii) values of a second plurality of features, of amino acids encoded by the wild-type polynucleotide sequence;
- wherein the features of the first plurality are the same as the features of the second plurality;
- training a machine learning classification algorithm using the differences;
- and
- determining, by the trained algorithm, a prediction of phenotype severity of the gene variant.
Dependencies are inferred from the claim text.