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US 2021/0232934 A1Published application
Systems and methods for enhanced user specific predictions using machine learning techniques
AssigneeColor Genomics, Inc.
InventorsCarmen Lai, Jill Hagenkord, Katsuya Noguchi
Life of this patent
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Abstract
Data sets can be processed using machine learning or artificial intelligence models to generate outputs predictive of a degree to which performing a protocol can positively modify an expected result associated with a condition. Generating the output may include accessing a user data set, inputting the user data set into a trained machine learning model to generate an output, and selecting an incomplete subset of a set of genes based on the output.
Claims20
A computer-implemented method, comprising:
- receiving, at a server, a first communication corresponding to a request to identify a gene panel that is specific to a user;
- in response to receiving the first communication, generating a second communication responsive to the request to identify the gene panel that is specific to the user, wherein identifying the gene panel includes:
- identifying a set of genes, each gene of the set of genes being associated with one or more diseases, and each disease of the one or more diseases being associated with one or more treatments;
- accessing a user data set associated with the user, the user data set including one or more user feature values, each user feature value of the one or more user feature values characterizing an aspect of the user;
- generating an input data set including a set of input data elements that are specific to the user, each input data element of the set of input data elements including the user data set, a gene of the set of genes, and a disease of the one or more diseases associated with the gene;
- inputting the input data set into a trained machine learning model, the trained machine learning model having been trained using a training data set associated with a plurality of other users;
- processing the input data set using the trained machine learning model, the processing including evaluating each input data element of the set of input data elements using one or more intermediate values, each intermediate value of the one or more intermediate values representing a computed value determined during processing of the input data set, wherein the evaluation includes classifying the gene included in the input data element, and the classification representing a prediction of a degree to which an expected event of the disease included in the input data element is modifiable by performing, on the user, any treatment of the one or more treatments associated with the disease;
- determining, based at least in part on the one or more intermediate values, that a particular user feature value in the user data set contributed to the classification of the gene included in the input data element;
- and
- selecting a subset of genes from amongst the set of genes to include in the gene panel specific to the user, each gene of the subset of genes being selected based on the particular user feature value and the classification of the gene;
- and
- transmitting the second communication in response to receiving the first communication.
9 more elements
A system, comprising:
- one or more processors;
- and
- a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more processors, cause the one or more processors to perform operations including:
- receiving a first communication corresponding to a request to identify a gene panel that is specific to a user;
- in response to receiving the first communication, generating a second communication responsive to the request to identify the gene panel that is specific to the user, wherein identifying the gene panel includes:
- identifying a set of genes, each gene of the set of genes being associated with one or more diseases, and each disease of the one or more diseases being associated with one or more treatments;
- accessing a user data set associated with the user, the user data set including one or more user feature values, each user feature value of the one or more user feature values characterizing an aspect of the user;
- generating a input data set including a set of input data elements that are specific to the user, each input data element of the set of input data elements including the user data set, a gene of the set of genes, and a disease of the one or more diseases associated with the gene;
- inputting the input data set into a trained machine learning model, the trained machine learning model having been trained using a training data set associated with a plurality of other users;
- processing the input data set using the trained machine learning model, the processing including evaluating each input data element of the set of input data elements using one or more intermediate values, each intermediate value of the one or more intermediate values representing a computed value determined during processing of the input data set, wherein the evaluation includes classifying the gene included in the input data element, and the classification representing a prediction of a degree to which an expected event of the disease included in the input data element is modifiable by performing, on the user, any treatment of the one or more treatments associated with the disease;
- determining, based at least in part on the one or more intermediate values, that a particular user feature value in the user data set contributed to the classification of the gene included in the input data element;
- and
- selecting a subset of genes from amongst the set of genes to include in the gene panel specific to the user, each gene of the subset of genes being selected based on the particular user feature value and the classification of the gene;
- and
- transmitting the second communication in response to receiving the first communication.
12 more elements
A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause a processing apparatus to perform operations including:
- receiving a first communication corresponding to a request to identify a gene panel that is specific to a user;
- in response to receiving the first communication, generating a second communication responsive to the request to identify the gene panel that is specific to the user, wherein identifying the gene panel includes:
- identifying a set of genes, each gene of the set of genes being associated with one or more diseases, and each disease of the one or more diseases being associated with one or more treatments;
- accessing a user data set associated with the user, the user data set including one or more user feature values, each user feature value of the one or more user feature values characterizing an aspect of the user;
- generating a input data set including a set of input data elements that are specific to the user, each input data element of the set of input data elements including the user data set, a gene of the set of genes, and a disease of the one or more diseases associated with the gene;
- inputting the input data set into a trained machine learning model, the trained machine learning model having been trained using a training data set associated with a plurality of other users;
- processing the input data set using the trained machine learning model, the processing including evaluating each input data element of the set of input data elements using one or more intermediate values, each intermediate value of the one or more intermediate values representing a computed value determined during processing of the input data set, wherein the evaluation includes classifying the gene included in the input data element, and the classification representing a prediction of a degree to which an expected event of the disease included in the input data element is modifiable by performing, on the user, any treatment of the one or more treatments associated with the disease;
- and
- determining, based at least in part on the one or more intermediate values, that a particular user feature value in the user data set contributed to the classification of the gene included in the input data element;
- and
- selecting a subset of genes from amongst the set of genes to include in the gene panel specific to the user, each gene of the subset of genes being selected based on the particular user feature value and the classification of the gene;
- and
- transmitting the second communication in response to receiving the first communication.
10 more elements
Dependencies are inferred from the claim text.