Method and device for training model for estimating polygenic risk score
AssigneeGenoplan Inc.
InventorsByung Kyu Kang, Byung Chul Lee, Sun Bin Kim, Joon Ho Kang, Myeong Jae Cheon
Life of this patent
Estimating the term…
Abstract
A method of training a model for estimating a polygenic risk score (PRS), according to an embodiment of the present disclosure, may construct a training data set including one or more pieces of training data in which genetic information about a single nucleotide polymorphism (SNP) is an input and a PRS calculation value corresponding to the genetic information is a label. Furthermore, the method may train the model for estimating the PRS, based on the training data set.
Claims8
A method of training a model for estimating a polygenic risk score (PRS), the method comprising:
- constructing a training data set comprising one or more pieces of training data in which genetic information about a single nucleotide polymorphism(SNP) is an input and a PRS calculation value corresponding to the genetic information is a label;
- and
- training the model for estimating the PRS, based on the training data set,
- wherein the training comprises:
- calculating the genetic information by using the model;
- obtaining, from the model, a PRS estimation value corresponding to the genetic information;
- and
- updating parameters for the model by performing backpropagation on the model, based on a loss function regarding an error between the PRS estimation value and the PRS calculation value, which is the label for the genetic information.
A device for training a model for estimating a polygenic risk score (PRS), the device comprising:
- a memory in which at least one program is stored;
- and
- a processor configured to execute the at least one program,
- wherein the processor is further configured to:
- construct a training data set comprising one or more pieces of training data in which genetic information about a single nucleotide polymorphism(SNP) is an input and a PRS calculation value corresponding to the genetic information is a label;
- and
- train the model for estimating the PRS, based on the training data set,
- wherein the processor is further configured to:
- calculate the genetic information by using the model;
- obtain, from the model, a PRS estimation value corresponding to the genetic information;
- and
- update parameters for the model by performing backpropagation on the model, based on a loss function regarding an error between the PRS estimation value and the PRS calculation value, which is the label for the genetic information.
Dependencies are inferred from the claim text.