KR20220099229A - Method and system for cold classification - Google Patents

Abstract

Disclosed are a cold sensitivity classification method and a cold sensitivity classification system. According to one embodiment of the present invention, the cold sensitivity classification method comprises: a step of classifying a target population into a plurality of groups through a conducted survey; a step of calculating a polygenic risk score (PRS) for the selected cold sensitivity index and for each of the plurality of groups; and a step of classifying the plurality of groups into a cold sensitivity group and a non-cold sensitivity group using the PRS. Therefore, the present invention is capable of accurately classifying cold sensitivity.

Description

COLD CLASSIFICATION METHOD AND COLD CLASSIFICATION

The present invention relates to a coldness classification method and a coldness classification system for classifying an individual's coldness degree using a genetic marker related to coldness.

In particular, the present invention provides a method for classifying coldness, which numerically shows the degree of coldness that feels chills for a group that is primarily filtered through a questionnaire, so that the group can be accurately classified into a coldness group or a coldness group and devices.

Poor circulation may refer to a condition in which hands or feet become cold at a temperature that other people do not feel cold, which may mean a condition in which inconvenience is great in daily life.

Such poor circulation is closely related to the onset of various diseases because gastrointestinal disorders, hypotension, water metabolism disorders, hyperlipidemia, and the like are causative diseases.

However, cold sensitivity has a disadvantage in that it is difficult to ensure accuracy in determining cold sensitivity because the scale for temperature sensitivity is still unclear.

Therefore, there is an urgent need to develop a classification model that accurately classifies poor circulation.

SUMMARY OF THE INVENTION An embodiment of the present invention has an object to be solved by providing a method for classifying cold symptoms and a system for classifying cold symptoms using genetic markers of poor circulation.

In addition, embodiments of the present invention, including genetic information that does not change from birth, aims to provide a more objective diagnostic model of poor circulation.

In addition, an embodiment of the present invention aims to make it possible to utilize as a decision support system during cold diagnosis using genetic indicators and clinical information according to the activation of the oriental genetic test market.

According to an embodiment of the present invention, a method for classifying poor circulation includes: classifying a target population into a plurality of groups through the conducted questionnaire; For each of the plurality of groups, calculating a Polygenic Risk Score (PRS) for the selected cold-related index; and classifying the plurality of groups into a cold disorder group and a cold disorder group by using the PRS.

In addition, according to an embodiment of the present invention, the cold classification system includes a division unit for dividing the target population into a plurality of groups through the conducted questionnaire; For each of the plurality of groups, a processing unit for calculating a Polygenic Risk Score (PRS) for the selected cold-related index; and a classification unit for classifying the plurality of groups into a cold disorder group and a cold disorder group by using the PRS.

According to an embodiment of the present invention, it is possible to provide a cold syndrome classification method and a cold syndrome classification system for accurately classifying a cold syndrome using a genetic marker of a cold syndrome.

In addition, according to an embodiment of the present invention, including genetic information that does not change from birth, it is possible to provide a more objective cold syndrome diagnosis model.

In addition, according to an embodiment of the present invention, it can be used as a decision support system during cold diagnosis using genetic indicators and clinical information according to the activation of the oriental genetic test market.

1 is a block diagram showing the configuration of a cold classification system according to an embodiment of the present invention.
2 is a diagram to show the difference regarding the distribution of the genetic influence of coldness and non-coldness.
3 is a diagram for explaining classification accuracy and ROC curve using 21 SNP genetic indicators.
4 is a diagram for explaining a cold syndrome classification process according to the present invention.
5 is a flowchart illustrating a method for classifying a cold, according to an embodiment of the present invention.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, since various changes may be made to the embodiments, the scope of the patent application is not limited or limited by these embodiments. It should be understood that all modifications, equivalents and substitutes for the embodiments are included in the scope of the rights.

Terms used in the examples are used for the purpose of description only, and should not be construed as limiting. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present specification, terms such as “comprise” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It is to be understood that it does not preclude the possibility of the presence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiment belongs. Terms such as those defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present application. does not

In addition, in the description with reference to the accompanying drawings, the same components are assigned the same reference numerals regardless of the reference numerals, and the overlapping description thereof will be omitted. In the description of the embodiment, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the embodiment, the detailed description thereof will be omitted.

1 is a block diagram showing the configuration of a cold classification system according to an embodiment of the present invention.

Referring to FIG. 1 , a cold classification system 100 according to an embodiment of the present invention may include a classification unit 110 , a processing unit 120 , and a classification unit 130 . In addition, the cold classification system 100 may be configured by selectively including the regulation unit 140 according to an embodiment.

First, the classification unit 110 divides the target population into a plurality of groups through the conducted questionnaire. That is, the classification unit 110 may serve to divide the users of the target population into a plurality of groups based on the results of the questionnaire related to measuring the chill felt by the user among the survey conducted in advance.

For example, with respect to the questionnaire related to poor circulation, "Give a score within the range of at least 1 to at most 7 points for the degree of feeling that your hands and feet feel cold indoors.", the division unit 110 groups users for each score Therefore, it can be divided into a plurality of groups.

Alternatively, with respect to the questionnaire related to poor circulation, "Where do you feel the most chill in the room?, 1. Hands, 2. Feet, 3. Face, 4. Head", the division unit 110 indicates '1. hand' and '2. Users who select 'foot' can be grouped into one group, and can be divided into a plurality of groups.

The processing unit 120 calculates a Polygenic Risk Score (PRS) for the selected cold-related index for each of the plurality of groups. That is, the processing unit 120 may serve to give a numerical value of the degree of feeling of chills to each of the previously classified groups.

Here, the Polygenic Risk Score (PRS) is a multiple-gene risk score, and may be a method of predicting the risk of onset by quantifying the location of hundreds of genes affecting the disease derived by the deletion value prediction technique and the risk of the disease.

Polygenic Risk Score (PRS) predicts the effects and effects of various SNPs through the combination of various SNPs for diseases that fail to find the remaining 1 or 2 SNPs through the GWAS study, since various factors affect most traits. It was introduced for risk stratification of diseases through this.

The cold-related index may refer to a genetic index sensitive to cold, and in the present invention, a SNP genetic index selected through a cold-related candidate group may be utilized.

To this end, the processing unit 120 may estimate a cold syndrome candidate group from among the plurality of groups, based on the result of the survey. That is, the processing unit 120 may select and estimate a specific group having a high probability of being confirmed as a cold-related group as a cold-related candidate group, from among the plurality of divided groups, based on the result of the survey.

For example, the processing unit 120 for the above-mentioned questionnaire item "The degree of feeling that the hands and feet are cold indoors, give a score in the range of a minimum of 1 point to a maximum of 7 points.", '7 points associated with a relatively high score. , 6 points' can be estimated as a cold syndrome candidate group.

Thereafter, the processing unit 120 may select 21 SNP genetic indices derived by analyzing the cold syndrome candidate group using a whole genome association analysis method (GWAS) as the cold disease association index. That is, the processing unit 120 may derive specific genetic indices to be used as the cold syndrome association index by analyzing the user belonging to the cold syndrome candidate group by using the whole genome association analysis method (GWAS).

Genome Wide Association Study (GWAS) can be a method to analyze and study the association between various genetic variants and specific traits that appear in various individuals within a population of a specific species. have.

Whole-genome association analysis (GWAS) may be a method to determine whether single nucleotide polymorphisms (SNPs) among genetic variations are associated with a specific trait at the genome level.

The various phenotypes of a particular trait are manifested by genetic variants of various genes. Traditional genetics examines the phenotypic information of offspring through crossbreeding experiments or phenotypic information between individuals in a pedigree to identify the genes responsible for various phenotypes of quantitative traits such as height, weight, color, and disease resistance. By looking at the relationship between the inheritance of a specific phenotype and the inheritance of alleles at different loci, we study the relationship.

This genetic mapping method is referred to as linkage analysis. The name association analysis is due to the fact that genetic mapping is based on detecting physical associations between genes, as measured by recombination rates, in offspring resulting from crosses.

Whole-genome association analysis (GWAS) looks for specific associations between diverse traits within a population and many alleles scattered across the genome. In addition, whole genome association analysis (GWAS) can directly compare associations between traits and specific allele groups in a population without relying on crosses or pedigrees.

According to an embodiment, the processing unit 120 may select a cold-related index after gender/age correction for the cold-disorder candidate group.

To this end, the processing unit 120 may correct the gender and age of the user belonging to the cold syndrome candidate group through logistic regression analysis. That is, the processing unit 120 may correct the gender/age of the user in the cold syndrome candidate group to correspond to a predetermined reference range.

Here, logistic regression is a statistical technique used to predict the probability of occurrence of an event using a linear combination of independent variables.

Logistic regression analysis expresses the relationship between the dependent variable and the independent variable as a specific function and is used in future prediction models. Because it is divided into , it can be called a kind of classification technique.

For example, the processing unit 120 uses logistic regression analysis to set a reference range by assigning a correction weight of '1' to male and female users corresponding to 30 to 50 years of age in the cold syndrome candidate group, and other age groups. For each male and female user, a correction weight of '1±α' (α is an integer greater than 0) is given according to a set condition, so that even male and female users who are out of the standard range can fall within the standard range, thereby contributing to the selection of indicators related to poor circulation. can get you to participate.

Thereafter, the processing unit 120 may derive the 21 SNP genetic indices in association with the cold syndrome after the correction. That is, the processing unit 120 may derive specific genetic indices to be used as a cold-related index by analyzing the users in the cold syndrome candidate group whose gender/age has been corrected by using the whole genome association analysis method (GWAS).

In addition, the classifying unit 130 classifies the plurality of groups into a cold disorder group and a cold disorder group by using the PRS. That is, the classification unit 130 may serve to classify a group having a relatively high value as a cold group by comparing the PRS in which the degree of feeling of chills is converted into a numerical value for each group.

In the classification of the cold/non-cold group, the cold classification system 100 may utilize a reference value obtained through the accuracy of the cutoff calculated in association with the PRS.

In order to define the reference value, the cold classification system 100 may be configured to additionally include a defining unit 140 .

The defining unit 140 may obtain an accuracy according to a cutoff by applying the calculated PRS to a classification model for each of the plurality of groups, and may define a cutoff section value having the highest accuracy as the reference value. That is, the regulating unit 140 learns the PRS and writes it as a modeling curve, and determines the value of the point with high accuracy as the reference value is small because the difference from the set value at the cutoff point at which the modeling curve of the curved PRS is inflected is small. can play a role In this case, the prescribed reference value may be determined to be at least a positive number.

Here, the classification model may exemplify a linear classification model, and may be a model for classifying groups into two or more groups using a line.

After defining the reference value, the classification unit 130 classifies the group in which the PRS is calculated exceeding the reference value, which is a positive number, as the cold group, and the group in which the PRS is calculated below the reference value is classified as the non-cold group. can That is, the classification unit 130 may determine only a group in which a PRS higher than a value prescribed as a reference value due to high accuracy is calculated as a cold syndrome group. The group to be determined as the cold-disease group may be at least a cold-disorder candidate group estimated through a previous survey.

According to an embodiment of the present invention, it is possible to provide a cold syndrome classification method and a cold syndrome classification system for accurately classifying a cold syndrome using a genetic marker of a cold syndrome.

In addition, according to an embodiment of the present invention, including genetic information that does not change from birth, it is possible to provide a more objective cold syndrome diagnosis model.

In addition, according to an embodiment of the present invention, it can be used as a decision support system during cold diagnosis using genetic indicators and clinical information according to the activation of the oriental genetic test market.

Hereinafter, an operation principle of the cold classification system 100 will be described in detail through experiments performed in relation to the cold classification system 100 .

The cold classification system 100 of the present invention, in step 1, for 733 people with poor circulation (a group with a high degree of cold hands and feet) among 2000 people obtained through cohort data collection, correction for gender and age After that, whole genome analysis (logistic regression) was performed.

In addition, the cold-disorder classification system 100 selected 21 cold-related indices through statistical significance in step 2, measured the genetic influence thereof, and used the selected index to obtain a Polygenetic Risk Score (PRS) for each sample. was calculated.

In addition, the cold classification system 100 compares the PRS distribution of the cold group and the cold group in step 3 to confirm that the genetic score is high in the cold group, and then applies the machine learning algorithm to AUC (Area Under the Curve) measured.

In addition, the cold disease classification system 100, in step 4, obtained a score through the genetic factors of the cold disease related index obtained through the genetic test, and was determined by applying to the cold disease classification model.

In addition, the cold classification system 100 selects 21 SNP genetic indices as shown in Table 1 as a result of the whole genome analysis in step 1.

SNP (Single Nucleotide Polymorphism) may refer to a phenomenon in which each person has a slightly different nucleotide sequence for the same gene. In general, since several base codons are possible when coding one amino acid, even if they have slightly different nucleotide sequences from each other, it is a big problem due to the generation of proteins with the same function. does not occur However, if the difference in base sequence is so great that a completely different amino acid is encoded or a change occurs in a part that affects the expression of a gene, it can be expressed as a genetic disease in the person concerned.

The cold classification system 100 may calculate the PRS in step 2 using the selected 21 SNP genetic indicators to obtain a genetic influence score for each sample.

If the existing disease classification algorithms are classified using the genotype itself, the genetic influence score of each of the 21 SNP genetic indicators can be used as a classification model in the cold classification system 100 of the present invention.

The difference in the genetic influence of coldness and non-coldness may show a higher score distribution in the cold group than in the non-cool group, as shown in FIG. 2 .

2 is a diagram to show the difference regarding the distribution of the genetic influence of coldness and non-coldness.

As shown in FIG. 2, cold has a lower density value than non-cold, but it can be expressed as a curve of uniform curvature over the entire area of SNPeffect.

That is, cold-disease may show a higher score distribution compared to non-cold disorder, and thus may be relatively superior in genetic influence.

The poor circulation classification system 100 may show an accuracy and a Receiver Operating Characteristic (ROC) curve as shown in FIG. 3 as a result of performing a cold classification classification using the score distribution.

3 is a diagram for explaining classification accuracy and ROC curve using 21 SNP genetic indicators.

In (a) of FIG. 3 , the accuracy of the score distribution is expressed, and the highest accuracy of 0.7 is shown at about a cutoff of 0.5 point.

In (b) of FIG. 3, the ROC (Receiver Operating Characteristic) curve for the score distribution is expressed, and as the false positive rate increases, the true positive rate also increases. The increasing slope may be gradually lowered as the false positive rate increases, as shown in FIG. 3B .

Cold, which can be said to be an individual's sensitivity to temperature, is the basis for diagnosis of oriental medicine.

In 1,753 twins (average age = 19.1±3.1), the heritability of cold syndrome (shiver fever) was 40%.

The present invention relates to finding a genetic index for poor circulation based on the dielectric constant of poor circulation, and classifying poor circulation using the genetic indicator.

4 is a diagram for explaining a cold syndrome classification process according to the present invention.

First, the cold classification system 100 may collect cold / non-cold data ( 410 ).

The cold classification system 100 secures the questionnaire data and clinical information data of 2000 people through the Daejeon area cohort data collection. The coldness classification system 100 determines that a person with a high coldness score who answers all 1 and 2 in items such as cold hands and cold feet as a questionnaire item (7-point scale) related to coldness is cold. The cold classification system 100 analyzes 733 people with poor circulation and 1,267 people with non-cold symptoms. In addition, the cold classification system 100 produces genome data for participants using the PMRA Asian SNP chip platform.

The cold classification system 100 may perform logistic regression analysis ( 420 ).

The cold classification system 100 performs gender and age correction (P < 0.00001) through logistic regression analysis for 733 poor people.

Logistic regression is a type of regression analysis technique that can be performed especially when the dependent variable is dichotomous among regression analysis for predictive analysis. Logistic regression is a statistical technique used to quantitatively explain the relationship between one dependent binary variable and one or more numeric, nominal, or ordinal independent variables using a logistic regression function. That is, logistic regression can investigate multivariate regression relationships between one dependent variable and several independent variables through logistic regression analysis.

The cold-disorder classification system 100 may select a cold-related index ( 430 ).

The cold disease classification system 100 analyzes using a genome-wide association study (GWAS) to select a cold disease-related index. In the case of PMRA asian chip, 800,000 SNPs are included, and in order to analyze a larger number of SNPs, the cold classification system 100 performs logistic regression for 10 million SNPs by performing imputation on the Asian Reference Panel. proceed

The poor circulation classification system 100 uses the gender and age phenotypes that are highly correlated with poor circulation as correction variables. The cold classification system 100 selects 21 SNPs and uses the BLUP algorithm.

The cold classification system 100 may calculate a Polygenic Risk Score (PRS) ( 440 ).

Although poor circulation is a single phenotype, it can be seen as a complex trait involving environmental factors in addition to genetic factors. It is difficult to classify cold syndrome based on genetic influence alone, and in particular, the influence of each indicator obtained in the GWAS study is negligible. For this reason, in the classification of poor circulation, it is necessary to provide an index that can take into account the multifactoriality of the variables used.

In the present invention, it is intended to calculate the effect through a combination of several SNPs that significantly acted on cold syndrome.

Measuring the influence of genetic factors is BLUP (BLUP method or GCTA software: Yang J, Lee SH, Goddard ME and Visscher PM. GCTA: a tool for Genome-wide Complex Trait Analysis. Am J Hum Genet. 2011 Jan 88 ( 1): 76-82) algorithm is used.

The present invention compares the distribution of Polygenic Risk Score (PRS) in the cold and non-cold groups. In addition, the present invention measures AUC by applying it to a machine learning algorithm.

The method and system for determining cold syndrome can be determined by obtaining a score through the genetic factor of a cold-related index obtained through genetic testing and applying it to a cold-disorder classification model.

The cold syndrome classification system 100 may classify the cold syndrome ( 450 ).

In the cold classification system 100, 21 SNP genetic indices were selected. The cold classification system 100 uses 21 SNP genetic indicators to calculate PGS to obtain a genetic influence score for each sample.

If the existing disease classification algorithms were classified using the genotype's own type (category data), in the present invention, one genetic influence score for 21 SNP genetic indicators was used for the classification model.

The difference in the genetic influence of coldness and non-coldness appears to show a higher score distribution for coldness than for coldness.

It can be seen that PRS on 21 SNP genetic indices had a negative effect in the non-cold group, and a positive influence in the cold group.

The present invention was applied to the classification model using the PRS value for each sample, and the accuracy according to the cutoff as shown in FIG. 3 could be obtained, and the cutoff section with the highest accuracy was 0.5.

In the present invention, it can be seen that the accuracy is 0.72 when the PRS value exceeds 0.5 when judged to be cold, and when the PRS value is less than 0.5, when judged to be non-cold.

Hereinafter, the work flow of the cold classification system 100 according to embodiments of the present invention will be described in detail in FIG. 5 .

The cold syndrome classification method according to the present embodiment may be performed by the cold disease classification system 100 .

5 is a flowchart illustrating a method for classifying a cold, according to an embodiment of the present invention.

First, the cold classification system 100 divides the target population into a plurality of groups through the conducted questionnaire ( 510 ). Step 510 may be a process of dividing users of the target population into a plurality of groups based on the results of the questionnaire related to measuring the chill felt by the user among the survey conducted in advance.

For example, for the questionnaire related to poor circulation, "Give me a score in the range of a minimum of 1 to a maximum of 7 points for the degree of feeling that hands and feet are cold indoors." By grouping, it can be divided into a plurality of groups.

Alternatively, for the questionnaire related to poor circulation, "Where is the part that feels the most chill in the room?, 1. Hands, 2. Feet, 3. Face, 4. Head", the cold classification system 100 is '1. hand' and '2. Users who select 'foot' can be grouped into one group, and can be divided into a plurality of groups.

In addition, the cold classification system 100 calculates, for each of the plurality of groups, a Polygenic Risk Score (PRS) for the selected cold-related index ( 520 ). Step 520 may be a process of assigning a numerical value to the degree of feeling of chills for each of the previously classified groups.

Here, the Polygenic Risk Score (PRS) is a multiple-gene risk score, and may be a method of predicting the risk of onset by quantifying the location of hundreds of genes affecting the disease derived by the deletion value prediction technique and the risk of the disease.

Polygenic Risk Score (PRS) predicts the effects and effects of various SNPs through the combination of various SNPs for diseases that fail to find the remaining 1 or 2 SNPs through the GWAS study, since various factors affect most traits. It was introduced for risk stratification of diseases through this.

The cold-related index may refer to a genetic index sensitive to cold, and in the present invention, a SNP genetic index selected through a cold-related candidate group may be utilized.

To this end, the poor circulation classification system 100 may estimate a poor circulation candidate group from among the plurality of groups, based on the results of the survey. That is, the cold-disorder classification system 100 may select and estimate a particular group having a high probability of being confirmed as a cold-disorder candidate group, based on the result of a survey, from among the plurality of divided groups.

For example, the cold classification system 100 provides a 'relatively high score for the above-mentioned questionnaire item "The degree of feeling that hands and feet feel cold indoors, in a range from a minimum of 1 point to a maximum of 7 points." A group of '7 points and 6 points' can be estimated as a cold syndrome candidate group.

Thereafter, the cold disease classification system 100 may select 21 SNP genetic indices derived by analyzing the cold syndrome candidate group using a whole genome association analysis method (GWAS) as the cold disease association index. That is, the cold-disorder classification system 100 may derive specific genetic indices to be used as cold-related association indexes by analyzing the users belonging to the cold syndrome candidate group by using the whole genome association analysis method (GWAS).

Genome Wide Association Study (GWAS) can be a method to analyze and study the association between various genetic variants and specific traits that appear in various individuals within a population of a specific species. have.

Whole-genome association analysis (GWAS) may be a method to determine whether single nucleotide polymorphisms (SNPs) among genetic variations are associated with a specific trait at the genome level.

The various phenotypes of a particular trait are manifested by genetic variants of various genes. Traditional genetics examines the phenotypic information of offspring through crossbreeding experiments or phenotypic information between individuals in a pedigree to identify the genes responsible for various phenotypes of quantitative traits such as height, weight, color, and disease resistance. By looking at the relationship between the inheritance of a specific phenotype and the inheritance of alleles at different loci, we study the relationship.

This genetic mapping method is referred to as linkage analysis. The name association analysis is due to the fact that genetic mapping is based on detecting physical associations between genes, as measured by recombination rates, in offspring resulting from crosses.

Whole-genome association analysis (GWAS) looks for specific associations between diverse traits within a population and many alleles scattered across the genome. In addition, whole genome association analysis (GWAS) can directly compare associations between traits and specific allele groups in a population without relying on crosses or pedigrees.

According to an embodiment, the cold-disorder classification system 100 may select a cold-related index after gender/age correction for the cold-disorder candidate group.

To this end, the cold syndrome classification system 100 may correct gender and age through logistic regression analysis for users belonging to the cold syndrome candidate group. That is, the cold-disorder classification system 100 may correct the gender/age of the user in the cold-disorder candidate group to correspond to a predetermined reference range.

Here, logistic regression is a statistical technique used to predict the probability of occurrence of an event using a linear combination of independent variables.

Logistic regression analysis expresses the relationship between the dependent variable and the independent variable as a specific function and is used in future prediction models. Because it is divided into , it can be called a kind of classification technique.

For example, the cold classification system 100 uses logistic regression analysis to set a reference range by assigning a correction weight of '1' to male and female users corresponding to the age of 30 to 50 in the cold candidate group, and other By assigning a correction weight of '1±α' (α is an integer greater than 0) according to a set condition for each male and female user in each age group, even male and female users who fall outside the standard range can fall within the standard range, so that may participate in the selection process.

Thereafter, the cold disease classification system 100 may derive the 21 SNP genetic indicators in association with the cold disease population after the correction. That is, the cold disease classification system 100 can derive specific genetic indices to be used as cold disease association indicators by analyzing the users in the cold syndrome candidate group whose gender/age has been corrected by using the whole genome association analysis method (GWAS).

Subsequently, the cold classification system 100 uses the PRS to classify the plurality of groups into a cold disorder group and a cold disorder group ( 530 ). Step 530 may be a process of classifying a group having a relatively high value into a cold group by comparing the PRS in which the degree of feeling of chills is converted into a numerical value for each group.

In the classification of the cold/non-cold group, the cold classification system 100 may utilize a reference value obtained through the accuracy of the cutoff calculated in association with the PRS.

In order to define a reference value, the cold classification system 100 applies the calculated PRS to a classification model for each of the plurality of groups, thereby obtaining the accuracy according to the cutoff, and the interval value of the cutoff having the highest accuracy, the reference value can be defined as That is, the cold classification system 100 learns the PRS and writes it as a modeling curve, and the value of the point with high accuracy because there is little difference from the set value at the cutoff point where the modeling curve of the curved PRS is inflected is used as a reference value. can play a deciding role. In this case, the prescribed reference value may be determined to be at least a positive number.

Here, the classification model may exemplify a linear classification model, and may be a model for classifying groups into two or more groups using a line.

After the standard value is defined, the cold disorder classification system 100 classifies a group in which the PRS is calculated exceeding the positive reference value as the cold disorder group, and classifies a group in which the PRS is calculated below the reference value as the non-cold disorder group can do. That is, the cold-disorder classification system 100 may determine only a group in which a PRS higher than a value prescribed as a reference value is calculated due to high accuracy as a cold-disorder group. The group to be determined as the cold-disease group may be at least a cold-disorder candidate group estimated through a previous survey.

According to an embodiment of the present invention, it is possible to provide a cold syndrome classification method and a cold syndrome classification system for accurately classifying a cold syndrome using a genetic marker of a cold syndrome.

In addition, according to an embodiment of the present invention, including genetic information that does not change from birth, it is possible to provide a more objective cold syndrome diagnosis model.

In addition, according to an embodiment of the present invention, it can be used as a decision support system during cold diagnosis using genetic indicators and clinical information according to the activation of the oriental genetic test market.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

The software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or device, to be interpreted by or to provide instructions or data to the processing device. , or may be permanently or temporarily embody in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

As described above, although the embodiments have been described with reference to the limited drawings, those skilled in the art may apply various technical modifications and variations based on the above. For example, the described techniques are performed in an order different from the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

100: cold classification system
110: division unit 120: processing unit
130: classification unit 140: regulation unit

Claims (11)

dividing the target population into a plurality of groups through the conducted survey;
For each of the plurality of groups, calculating a Polygenic Risk Score (PRS) for the selected cold-related index; and
Classifying the plurality of groups into a cold group and a cold group by using the PRS
A method of classifying cold symptoms, including. According to claim 1,
estimating a cold syndrome candidate group from among the plurality of groups based on a result of the survey; and
Selecting 21 SNP genetic indices derived by analyzing the cold syndrome candidate group using a genome-wide association study (GWAS) as the cold disease association index
A cold classification method further comprising a. According to claim 1,
correcting gender and age through logistic regression analysis with respect to the user belonging to the cold syndrome candidate group; and
Deriving the 21 SNP genetic markers in association with the cold syndrome population after the correction
A cold classification method further comprising a. According to claim 1,
classifying a group in which the PRS is calculated to exceed a reference value of a positive number into the cold disorder group; and
Classifying the group in which the PRS is calculated to be less than or equal to the reference value as the non-cold group
A cold classification method further comprising a. 5. The method of claim 4,
obtaining accuracy according to cutoff by applying the calculated PRS to a linear model for each of the plurality of groups; and
defining a section value of the cutoff having the highest accuracy as the reference value
A cold classification method further comprising a. a division unit for dividing the target population into a plurality of groups through the conducted survey;
For each of the plurality of groups, a processing unit for calculating a Polygenic Risk Score (PRS) for the selected cold-related index; and
A classification unit for classifying the plurality of groups into a cold group and a cold group by using the PRS
A cold classification system comprising a. 7. The method of claim 6,
The processing unit,
based on the results of the survey, estimate a cold syndrome candidate group from among the plurality of groups;
21 SNP genetic indicators derived by analyzing the cold syndrome candidate group using the whole genome association analysis method (GWAS) are selected as the cold disease association indicators.
Cold classification system. 7. The method of claim 6,
The processing unit,
For users belonging to the cold syndrome candidate group, gender and age are corrected through logistic regression analysis,
In association with the cold disease population after the correction, to derive the 21 SNP genetic indicators
Cold classification system. 7. The method of claim 6,
The classification unit,
A group in which the PRS is calculated in excess of the positive reference value is classified as the cold group,
Classifying the group in which the PRS is calculated to be less than or equal to the reference value as the non-cold group
Cold classification system. 10. The method of claim 9,
For each of the plurality of groups, by applying the calculated PRS to the classification model, the accuracy according to the cutoff is obtained, and a regulating unit defining the section value of the cutoff having the highest accuracy as the reference value.
A cold classification system further comprising a. A computer-readable recording medium recording a program for executing the method of any one of claims 1 to 5.

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