Method for extracting intrinsic properties of cancer cells from gene expression profiles of cancer patients and device for the same

A method of predicting a condition of a patient, comprising:

• decomposing, by a computing device, an input matrix into a sum of a product of a plurality of element matrices and one residual matrix by using a predetermined algorithm, the input matrix representing expression levels of a plurality of genes of a plurality of patients and each of the patients;
• training, by the computing device, a classifier by supervised learning by using a plurality of health condition values representing health conditions of the plurality of patients as learning criteria and inputting the residual matrix into the input layer of the classifier, in order to train the classifier receiving a matrix having the same dimension as the residual matrix by the supervised learning;
• and
• obtaining, by the computing device, a value for predicting a health condition of a first patient, by inputting a first input matrix into an input layer of the classifier or inputting a first residual matrix into the input layer of the classifier, the first input matrix including the expression levels of genes of the first patient of which a health condition is required to be predicted, the first residual matrix being a residual matrix obtained from the first input matrix by using the algorithm,
• wherein the predetermined algorithm is an algorithm for decomposing a matrix input to the algorithm into a sum of a product of a plurality of element matrices and one residual matrix.

A system for predicting a health condition of a patient, comprising:

• a gene expression level analysis device;
• and
• a computing device,
• wherein the gene expression level analysis device is configured to analyze expression levels of genes of a first patient of which a health condition is required to be predicted, and analyze expression levels of genes from respective cells obtained from a plurality of patients,
• the computing device comprises:
• a residual generation unit configured to output one residual matrix by receiving an input matrix representing the plurality of patients and expression levels of a plurality of genes of each of the patients and decomposing the input matrix into a sum of a product of a plurality of element matrices and the residual matrix;
• and
• a classifier configured to receive a matrix having the same dimension as the residual matrix, the classifier being trained by a predetermined supervised learning method;
• and
• a processing unit,
• the processing unit is configured to:
• generate a first input matrix including the expression levels of genes of the first patient based on the expression levels of genes analyzed for the first patient, by receiving the expression levels of genes analyzed for the first patient from the gene expression level analysis device;
• and
• calculate a value for predicting the health condition of the first patient by inputting the generated first input matrix into an input layer of the classifier, and
• the predetermined supervised learning method comprises:
• analyzing, by the gene expression level analysis device, expression levels of genes from respective cells obtained from a plurality of patients;
• calculating, by the computing device, an input matrix representing the plurality of patients and expression levels of a plurality of genes of each of the patients based on the expression levels of genes analyzed for the plurality of patients, by receiving the expression levels of genes analyzed for the plurality of patients from the gene expression level analysis device;
• decomposing, by the residual generation unit of the computing device, the input matrix into a sum of a product of a plurality of element matrices and one residual matrix by using a predetermined algorithm;
• and
• training, by the computing device, the classifier by supervised learning by using a plurality of health condition values representing health conditions of the plurality of patients as learning criteria and inputting the residual matrix into the input layer of the classifier, in order to train the classifier by the supervised learning.