Identifying Possible Disease-Causing Genetic Variants by Machine Learning Classification
The techniques described herein relate identification of disease-causing genetic variant by machine learning classification. The techniques may include receiving a training dataset of predetermined variants associated with disease. A hyperplane is identified having a maximum margin between points of the dataset. Patient input data is received including an observed variant of a gene. Features of the observed variant are selected, and a score is determined The score is determined using Support Vector Machine algorithms based on an observation of a novel non-linear relationship with the selected features of the observed variant. The observed variant may be classified based on the score indicating a distance of the observed variant from the identified hyperplane.
1 . A method for identifying a possible disease-causing genetic variant by machine learning classification, comprising:
receiving a training dataset of predetermined variants associated with disease;
identifying a hyperplane having a maximum margin between points of the training dataset;
receiving patient input data comprising an observed variant of a gene;
selecting features of the observed variant;
determining a hyperplane score using Support Vector Machine algorithms based on an observation of a novel non-linear relationship with the selected features of the observed variant; and
classifying the observed variant as deleterious or tolerable based on the score indicating a distance of the observed variant from the hyperplane.
2 . The method of claim 1 , wherein the features comprise one or more of:
a value indicating the likelihood that the gene of the observed variant causes disease;
a value or values indicating specific sequence features;
a distance value indicating the distance of the observed variant to a transcription start site;
a likelihood that an amino acid substitution is associated with a disruption of the protein of the observed variant;
a predictive deleteriousness value of an algorithm;
a presence or absence of the observed variant in clinical databases;
a frequency of the observed variant in population databases;
a value indicating whether the variant disrupts intronic sequences controlling the proper splicing of the gene.
3 . The method of claim 1 , wherein the observation of a novel non-linear relationship with the selected features of the observed variant comprises a linear separability derived from an expanded input feature space of one or more kernel functions.
4 . The method of claim 1 , further comprising determining a phenotype adjusted gene score, wherein determining a phenotype score comprises:
identifying the gene containing the observed variant;
identifying occurrences of phenotypes associated with the gene within one or more databases; and
assigning a weight according to the relevance of the association.
5 . The method of claim 1 , further comprising determining a phenotype adjusted score, wherein determining a phenotype adjusted score comprises the square root of the multiplication of the hyperplane score by the phenotype adjusted gene score.
6 . The method of claim 1 , further comprising determining a family adjusted score, wherein determining a family adjusted score comprises:
determining a frequency of the observed variant within a family;
determining a family adjusted score of the observed variant based on a relationship between determined hyperplane score and the determined frequency within the family.
7 . The method of claim 6 , further comprising determining a family adjusted gene score, wherein determining a family adjusted gene score comprises aggregation of the family adjusted score of all variants which locate in the gene.
8 . The method of claim 7 , further comprising determining a gene phenotype combined score, wherein determining the gene phenotype combined score comprises the square root of the multiplication of the family adjusted gene score by the phenotype adjusted gene score.
9 . A system for identifying a possible disease-causing genetic variant by machine learning classification, comprising:
a processing device;
a storage device having instructions thereon that, when executed by the processing device, cause the system to:
receive a training dataset of predetermined variants associated with a disease;
identify a hyperplane having a maximum margin between points of the training dataset;
receive patient input data comprising an observed variant;
select features of the observed variant;
determine a score using Support Vector Machine algorithms based on an observation of a novel non-linear relationship with the selected features of the observed variant; and
classify the observed variant as deleterious or tolerable based on the score indicating a distance of the observed variant from the hyperplane.
10 . The system of claim 1 , wherein the features comprise one or more of:
a value indicating the likelihood that the gene of the observed variant causes disease;
a value or values indicating specific sequence features;
a distance value indicating the distance of the observed variant to a transcription start site;
a likelihood that an amino acid substitution is associated with a disruption of the protein of the observed variant;
a deleteriousness value of an algorithm;
a presence or absence of the observed variant in clinical databases;
a frequency of the observed variant in population databases;
a value indicating whether the variant disrupts intronic sequences controlling the proper splicing of the gene.
11 . The system of claim 10 , wherein the data of the features are based on data of third party databases.
12 . The system of claim 9 , wherein the observation of a novel non-linear relationship with the selected features of the observed variant comprises a linear separability derived from an expanded input feature space of one or more kernel functions.
13 . The system of claim 9 , the storage device further comprising instructions to cause the processing device to determine a phenotype adjusted gene score, wherein determining a phenotype score comprises:
identifying the gene containing the observed variant;
identifying occurrences of phenotypes associated with the gene within one or more databases; and
assigning a weight according to the relevance of the association.
14 . The system of claim 9 , the storage device further comprising instructions to cause the processing device to determine a phenotype adjusted score, wherein determining a phenotype adjusted score comprises the square root of multiplying the hyperplane score by the phenotype adjusted gene score.
15 . The system of claim 9 , the storage device further comprising instructions to cause the processing device to determine a family adjusted score, wherein determining a family adjusted score comprises:
determining a frequency of the observed variant within a family;
determining a family adjusted score of the observed variant based on a relationship between determined hyperplane score and the determined frequency within the family.
16 . The system of claim 15 , the storage device further comprising instructions to cause the processing device to determine a family adjusted gene score, wherein determining a family adjusted gene score comprises aggregation of the family adjusted score of all variants which locate in the gene.
17 . The system of claim 16 , the storage device further comprising instructions to cause the processing device to determine a gene phenotype combined score, wherein determining the gene phenotype combined score comprises the square root of multiplying the family adjusted gene score by the phenotype adjusted gene score.
18 . A non-transitory computer-readable medium for identifying a possible disease-causing genetic variant by machine learning classification, the computer-readable medium comprising processor-executable code to:
receive a training dataset of predetermined variants associated with a disease;
identify a hyperplane having a maximum margin between points of the training dataset;
receive patient input data comprising an observed variant;
select features of the observed variant;
determine a score using Support Vector Machine algorithms based on an observation of a novel non-linear relationship with the selected features of the observed variant; and
classify the observed variant as deleterious or tolerable based on the score indicating a distance of the observed variant from the hyperplane.
19 . The computer-readable medium of claim 18 , wherein the features comprise one or more of:
a value indicating the likelihood that the gene of the observed variant causes disease;
a value or values indicating specific sequence features;
a distance value indicating the distance of the observed variant to a transcription start site;
a likelihood that an amino acid substitution is associated with a disruption of the protein of the observed variant;
a deleteriousness value of an algorithm;
a presence or absence of the observed variant in clinical databases;
a frequency of the observed variant in population databases;
a value indicating whether the variant disrupts intronic sequences controlling the proper splicing of the gene.
20 . The computer-readable medium of claim 18 , wherein the data of the features are based on data of third party databases, wherein the observation of a novel non-linear relationship with the selected features of the observed variant comprises a linear separability derived from an expanded input feature space of one or more kernel functions.
21 . The computer-readable medium of claim 18 , the computer-readable medium further comprising processor-executable code to determine one or more of:
a phenotype adjusted gene score;
a phenotype adjusted score;
a family adjusted score, wherein determining a family adjusted score;
a family adjusted gene score; and
a gene phenotype combined score.