KR102278528B1 - Apparatus and systems for verifying the efficacy of a drug and food acting on a target mechanism using artificial intelligence - Google Patents

Abstract

A method for verifying the effectiveness of a drug acting on a target gene using artificial intelligence is provided by one disclosure, and the method includes periodically acquiring medical data of patients who received a first drug prepared to cure a disease. Step, classifying treatment data of patients whose disease was cured after administration of the first drug into a first category and medical data of patients whose disease was not cured after administration of the first drug into a second category among medical data; Deriving a target gene from the genes of patients included in the first category by using the first artificial intelligence learning model that learned the relationship between the gene expression distribution and the gene expression distribution by the components of the first drug, in the second category Obtaining a biomarker profile by analyzing the expression state of the target gene and the expression state of other genes by the first drug from the gene expression information of the included patients, and the target gene and components of the first drug based on the biomarker profile and determining whether to administer the second drug and the content of the second drug that can be used to treat diseases of patients included in the second category by using the second artificial intelligence learning model that has learned the relationship with the content. may include

Description

Apparatus and systems for verifying the efficacy of a drug and food acting on a target mechanism using artificial intelligence

According to one disclosure, the present invention provides an apparatus and method for verifying the effectiveness of a specific drug and deriving a new drug by analyzing the gene of a patient group in which symptoms are not alleviated and a patient group in which symptoms are not alleviated after receiving a specific drug using artificial intelligence is about

An artificial intelligence (AI) system is a computer system that implements human-level intelligence, and unlike the existing rule-based smart system, the machine learns, judges, and becomes smarter by itself. As artificial intelligence systems are used, the recognition rate improves and users can understand user preferences more accurately, so existing rule-based smart systems are gradually being replaced by deep learning-based artificial intelligence systems.

Artificial intelligence technology consists of machine learning (deep learning) and elemental technologies using machine learning. Machine learning is an algorithm technology that categorizes/learns the characteristics of input data by itself, and element technology is a technology that uses machine learning algorithms such as deep learning, such as linguistic understanding, visual understanding, reasoning/prediction, knowledge expression, motion control, etc. It consists of the technical fields of

The various fields where artificial intelligence technology is applied are as follows. Linguistic understanding is a technology for recognizing and applying/processing human language/text, and includes natural language processing, machine translation, dialogue system, question and answer, and speech recognition/synthesis. Visual understanding is a technology for recognizing and processing objects like human vision, and includes object recognition, object tracking, image search, human recognition, scene understanding, spatial understanding, image improvement, and the like. Inferential prediction is a technology for logically reasoning and predicting by judging information, and includes knowledge/probability-based reasoning, optimization prediction, preference-based planning, and recommendation. Knowledge expression is a technology that automatically processes human experience information into knowledge data, and includes knowledge construction (data generation/classification) and knowledge management (data utilization). Motion control is a technology for controlling autonomous driving of a vehicle and movement of a robot, and includes motion control (navigation, collision, driving), manipulation control (action control), and the like.

With the advancement of artificial intelligence and digital technology, health care technology is undergoing major changes, and effective management of new data by creating, collecting and tracking new data has become an important task. In particular, in the case of medical technology, the importance of not only personal health but also various industrial scalability and health welfare management is increasing, so interest in digital medical technology that can manage medical data and derive appropriate medical diagnosis is increasing. are doing

Korean Patent Publication No. 2019-0043639 (published on April 19, 2019) Republic of Korea Patent Registration Publication No. 2019-0036620 (published on April 5, 2019)

The technical problem of the present invention is to provide a device and method for effectively analyzing the effectiveness of a drug by monitoring whether the disease is cured or not for a drug specialized for a specific disease and a patient receiving the drug.

According to a first embodiment, there is provided a method of verifying the effectiveness of a drug acting on a target gene using artificial intelligence, wherein the method periodically records medical data of patients who received a first drug prepared to cure a disease. classifying medical data of patients whose disease was cured after administration of the first drug into a first category and medical data of patients whose disease was not cured after administration of the first drug into a second category among medical data; Deriving a target gene from the genes of patients included in the first category by using the first artificial intelligence learning model that learned the correlation between the gene expression distribution by disease and the gene expression distribution by the component of the first drug; Obtaining a biomarker profile by analyzing the expression state of the target gene by the first drug and the expression state of other genes from the gene expression information of patients included in category 2, and the target gene and the first gene based on the biomarker profile Using the second artificial intelligence learning model that learned the relationship with the component and content of the drug, determine whether to administer the second drug and the content of the second drug that can be used to treat diseases of patients included in the second category may include the step of

According to a second embodiment, there is provided an apparatus for verifying the effectiveness of a drug acting on a target gene using artificial intelligence, the apparatus comprising a processor and a memory for storing executable instructions, wherein the processor executes the instructions, Medical data of patients receiving a first drug prepared to cure a disease is periodically acquired, and medical data of patients cured of disease after administration of the first drug among the medical data is set to a first category, after administration of the first drug Classify the treatment data of patients whose disease has not been cured into the second category, and use the first AI learning model that learned the gene expression distribution, the first drug component, the first drug content, and the relationship between disease and cell function Thus, the target gene is derived from the genes of patients included in the first category, and the expression status of the target gene by the first drug and the expression status of other genes are analyzed from the gene expression information of the patients included in the second category. Obtaining a biomarker profile and using the second artificial intelligence learning model that learned the relationship between the target gene and the component and content of the first drug based on the biomarker profile, it is used to treat diseases of patients included in the second category. At least one of the amount of the first drug that can be used, whether the second drug is administered, and the content of the second drug may be determined.

According to the third embodiment, it is possible to provide a computer-readable non-transitory recording medium in which a program for implementing a method of verifying the effectiveness of a drug acting on a target gene using artificial intelligence is recorded.

According to one disclosure, the effect of a drug can be verified by tracking whether patients with the same disease using the same drug are cured of the disease. In particular, the same genome of a group of patients whose disease is confirmed to be cured is analyzed and the target gene of the disease is derived. It is possible to derive a different prescription for a group of patients whose disease has not been cured.

In addition, by deriving a target gene of a disease according to the present invention and analyzing the same genome of a patient group that has not been cured of the disease, it is possible to provide an effect of deriving other drugs that can be effectively applied to the genome.

According to one disclosure, it is possible to analyze medical data based on existing medical information, and build a digitization platform of various medical information based on the result of analyzing the bio-signals obtained from the user.

1 is a view for explaining the flow of a method of verifying the effectiveness of a drug acting on a target gene using artificial intelligence according to one disclosure.
2 is a view for explaining the characteristics of generating a first artificial intelligence learning model by one disclosure.
3 is a view for explaining the characteristics of generating a second artificial intelligence learning model by the start.
4 is a diagram for explaining a pathway until a drug acts on a gene according to one disclosure.
5 is a diagram for explaining the flow of a method of verifying the effectiveness of a drug according to one disclosure and recommending another drug.
6 is a view for explaining the configuration of a device for verifying the effectiveness of a drug acting on a target gene using artificial intelligence according to the disclosure.
7 is a view for explaining the configuration of a processor that generates an artificial intelligence learning model through data learning according to one disclosure.

Hereinafter, with reference to the accompanying drawings, embodiments of the present invention will be described in detail so that those of ordinary skill in the art can easily implement them. However, the present invention may be implemented in several different forms and is not limited to the embodiments disclosed below. In addition, in order to clearly disclose the present invention in the drawings, parts irrelevant to the present invention are omitted, and the same or similar symbols in the drawings indicate the same or similar components.

Objects and effects of the present invention can be naturally understood or made clearer by the following description, and the objects and effects of the present invention are not limited only by the following description.

The objects, features and advantages of the present invention will become more apparent from the following detailed description. In addition, in describing the present invention, when it is determined that a detailed description of a known technology related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted. Hereinafter, an embodiment according to the present invention will be described in detail with reference to the accompanying drawings.

Hereinafter, the apparatus 100 for verifying the effectiveness of a drug acting on a target gene using artificial intelligence will be abbreviated as the drug effectiveness verification apparatus 100 .

1 is a view for explaining the flow of a method of verifying the effectiveness of a drug acting on a target gene using artificial intelligence according to one disclosure.

According to one disclosure, in block 101, the drug validation apparatus 100 may periodically acquire medical data of patients who have received the first drug prepared to cure the disease.

By one disclosure the disease is, for example, cancer, a neoplasm, a tumor, and/or a metastatic form therein, a metabolic disorder, an inflammatory disease, or an infectious disease. In some embodiments, the cancer, neoplasm, tumor, or metastatic form therein is, for example, leukemia, lymphoma, prostate cancer, lung cancer, breast cancer, liver cancer, colorectal cancer, or kidney cancer. It may include all other diseases that occur in the human body.

According to one disclosure, the first drug is a drug formulated with a component suitable for curing a disease, and effectively acts on a target gene of the disease.

According to one disclosure, in block 102, the drug validation device 100 sets the medical data of patients whose disease is cured after administration of the first drug among the medical data into the first category, and the medical data of patients whose disease is not cured after administration of the first drug. The medical treatment data may be classified into the second category.

The patients included in the first category according to the disclosure refer to patients whose disease is cured by administering the first drug prepared from a predetermined component. Here, the patients included in the first category did not receive the first drug by the same dosage regimen, but may have received the first drug in a different dosage regimen according to each patient's condition. Patients included in the first category had their disease cured by the first drug, indicating that the first drug effectively acted on a specific gene of the patients included in the first category.

The patients included in the second category according to the disclosure include patients whose disease is not cured despite receiving the first drug prepared from a predetermined ingredient. Here, the patients included in the second category did not receive the first drug according to the same dosage regimen, but may have received the first drug in a different dosage regimen according to each patient's condition. In patients included in the second category, the disease was not cured by the first drug, indicating that the first drug did not effectively act on a specific gene of the patients included in the second category. That is, the patients included in the second category are patients in need of another method of curing a disease by improving the existing first drug or taking another drug.

According to one disclosure, in block 103, the drug validation device 100 uses a first artificial intelligence learning model that learns the correlation between the gene expression distribution by disease and the gene expression distribution by the component of the first drug, A target gene can be derived from the genes of patients included in the category.

According to one disclosure, the first artificial intelligence learning model is learned to derive a target gene of a disease, and the change in the expression state of the gene due to the disease, the change in the expression state of the gene due to the first drug, and the other due to the first drug It is obtained based on the result of learning (unsupervised learning) the relationship between the change in the expression state of the gene.

A gene to be analyzed in DNA by initiation may include MDGA1 (MAM domain containing glycosylphosphatidylinositol anchor 1). The MDGA1 may be composed of a polynucleotide including bases 37617785 to 37618223 on chromosome 6 or a polynucleotide complementary thereto. In addition, the target gene may further include cytochrome P450 family 4 subfamily V member 2 (CYP4V2). The CYP4V2 may be composed of a polynucleotide including bases 187124479 to 187125005 on chromosome 4 or a complementary polynucleotide thereof. The target gene may further include one or more of Arf-GAP with dual PH domain-containing protein 1 (ADAP1), LIM homeobox 6 (LHX6), and Ras GTPase-activating protein 3 (RASA3). The ADAP1 may be composed of a polynucleotide including bases 948157 to 949189 on chromosome 7 or a complementary polynucleotide thereof. The LHX6 may be composed of a polynucleotide including bases 124989781 to 124989950 on chromosome 9 or a polynucleotide complementary thereto. The RASA3 may be composed of a polynucleotide including bases 114813885 to 114814341 on chromosome 13 or a polynucleotide complementary thereto.

The target gene may further include one or more of a polymeric immunoglobulin receptor (PIGR), a disintegrin and metalloproteinase domain-containing protein 12 (ADAM12), and a chromosome 21 open reading frame 128 (C21orf128). In addition, PIGR may be composed of a polynucleotide including the base 207105468 to the base 207105762 on chromosome 1 or a polynucleotide complementary thereto. The ADAM12 may be composed of a polynucleotide including bases 127822964 to 127823174 on chromosome 10 or a polynucleotide complementary thereto. The C21orf128 may be composed of a polynucleotide including base 43528602 to base 43529012 on chromosome 21 or a complementary polynucleotide thereof.

Additionally, the target gene may be a gene present on chromosome 2. Specifically, the target gene may further include at least one of G protein-coupled receptor 35 (GPR35) and stonin 1 (STON1).

GPR35 may consist of a polynucleotide including bases 241562679 to 241562995 on chromosome 2 or a polynucleotide complementary thereto. The STON1 may be composed of a polynucleotide including bases from 48983601 to 48983821 on chromosome 2 or a polynucleotide complementary thereto. The target gene may further include Parkin coregulated gene protein (PACRG) present on chromosome 6 .

According to one disclosure, in block 104, the drug validation device 100 analyzes the expression state of the target gene and the expression state of other genes by the first drug from the gene expression information of the patients included in the second category to biomarker profile can be obtained.

According to one disclosure, the biomarker profile refers to data for revealing the reason why the same disease was not cured with the same drug. For example, it is assumed that gene 6 is determined as a target gene from people who have been cured by the first drug. For those included in the second category, the disease was not cured by the first drug, because the modified state (expression or suppression) of gene 6 was not to the extent that the disease could be cured. However, if the people included in the second category had mutations in gene 12 in common, data on the relationship between the first drug and gene 12 could be a biomarker profile, and gene 12 could be a biomarker. have.

According to one disclosure, in block 105, the drug validation device 100 uses a second artificial intelligence learning model that learns the association between the target gene and the component and content of the first drug based on the biomarker profile, the second category It is possible to determine whether to administer the second drug and the content of the second drug that can be used to treat diseases of patients included in the .

That is, the drug validation device 100 cannot heal the patients included in the second category in the same way as the patients included in the first category, so other treatments are used to heal the patients included in the second category. way can be devised. Since the second artificial intelligence learning model is generated using medical treatment data of patients whose disease has not been cured even after the administration of the first drug, a new treatment method for each of the patients included in the second category can be created based on this. can

According to one disclosure, the second drug may include a drug having the same component as the first drug but having a different content of the component, and may be a drug including a component not included in the first drug. The second drug is used for the purpose of curing the same disease that acts on the first drug. According to one disclosure, the second drug may include at least one new drug.

In some embodiments, the computer-based analysis program converts raw data generated by a detection assay (eg, the presence, absence, or amount of a given marker or marker) to data values for clinical use (eg, a drug target or drug). used to translate into (s) optional). A clinician may access the data using any suitable means. Thus, in some preferred embodiments, the present invention provides the additional advantage that a clinician without training in genetics or molecular biology does not need to understand the raw data. The data is provided directly to the clinician in its most useful form. The clinician can then use the information immediately to optimize the subject's healing.

The present invention contemplates any method that can receive, process, and transmit information to or from the laboratory performing the assay, information providers, health care workers, and subjects. For example, in some embodiments of the invention, a sample (eg, a biopsy or other sample) is obtained from a subject and is located anywhere in the world (eg, the country in which the subject resides or in which the information is ultimately used). to a profiling service (eg, a clinical laboratory in a medical facility, a genomic profiling business, etc.) located in a country other than If the sample comprises a tissue or other biological sample, the subject may visit a medical center to provide the sample and send it to a profiling center, or the subject may collect a sample (eg, a urine sample) by itself and , you can send it directly to the profiling center. If the sample already contains measured biological information, that information can be communicated by the subject to a profiling center (eg, by scanning an information card containing the information into a computer and sending the data to an electronic communication system). After delivery to the profiling service, the sample is processed and a profile specific to the diagnostic, therapeutic or prognostic information desired for the subject is generated (e.g., a protein expression data).

In accordance with one disclosure the profile is generated in a format suitable for interpretation by a attending physician. For example, without providing raw expression data, the resulting format may represent a proposed therapeutic course of action (eg, a specific drug for administration). The data may be presented to the clinician by any suitable method. For example, in some embodiments, the profiling service generates a report that can be printed for the clinician (eg, at the point of care) or presented to the clinician on a computer monitor.

In some embodiments, the information may be first analyzed at the treatment site or at a local facility. The raw data is then forwarded to a central processing facility for further analysis and/or to convert the raw data into useful information for the clinician or patient. The central processing facility offers the advantages of privacy (all data is stored in a central facility with uniform security protocols), speed and uniformity of data analysis. The central processing facility can then control the fate of the data after treatment of the subject. For example, using an electronic communication system, the central facility may provide data to a clinician, subject, or researcher.

2 is a view for explaining the characteristics of generating a first artificial intelligence learning model by one disclosure.

According to one disclosure, the first artificial intelligence learning model is learned to derive a target gene of a disease, and the change in the expression state of the gene due to the disease, the change in the expression state of the gene due to the first drug, and other genes due to the first drug It is obtained based on the result of learning (unsupervised learning) the relationship between the change in the expression state of

Drug validation device 100 is based on the change in the expression state of the gene due to the disease, the change in the expression state of the gene due to the first drug, the change in the expression state of other genes due to the first drug, the function of the cell and the type of disease Iterative learning can be performed to derive a target gene of a disease. The drug validation apparatus 100 may generate a first AI learning model for deriving a target gene of a disease after repeated learning.

The first artificial intelligence learning model may be generated as a result of unsupervised learning of the initial deep neural network by inputting learning data into the input layer of the initial deep neural network, and an intermediate deep neural network with an updated initial connection strength. , and the learning data may be expression change gene information for each of a plurality of gene datasets.

Using a Deep Belief Network method to learn the current layer by performing propagation and backpropagation in the current layer by one start, and learning the next layer of the current layer when the learning of the current layer is completed The initial connection strength may be updated.

The drug validation device 100 sets the learning data to the input layer of the intermediate deep neural network, sets the output value specified for the type of learning data input to the input layer to the output layer of the intermediate deep neural network, and the input layer of the intermediate deep neural network A final deep neural network may be generated by supervised learning the intermediate deep neural network based on the value set in the output layer and the output layer.

The type of learning data by the drug validation device 100 includes any one of a first type representing a normal genetic dataset extracted from a normal group and a second type representing a disease gene dataset extracted from a patient group, and the learning unit When the training data corresponding to the first type is input to the input layer of the intermediate deep neural network, the middle deep neural network is trained by setting the first output value in the output layer of the intermediate deep neural network, and the training data corresponding to the second type is applied to the intermediate deep neural network. In the case of input to the input layer of the neural network, the intermediate deep neural network may be trained by setting the second output value in the output layer of the intermediate deep neural network.

The drug validation device 100 compares the predicted value obtained by calculating forward propagation in the input layer of the intermediate deep neural network with the output value set in the output layer of the intermediate deep neural network to obtain an error and then back propagation in the direction of minimizing the error Thus, each connection strength of the intermediate deep neural network can be updated.

3 is a view for explaining the characteristics of generating a second artificial intelligence learning model by the start.

According to one disclosure, the drug validation device 100 is a component of the first drug, the content of the first drug, the change in the expression state of the target gene due to the first drug, the change in the expression state of another gene due to the first drug, the second Repeated learning can be performed to find other treatment methods for diseases based on the components and contents of the drug, whether the disease is alleviated or not, and the function of cells. The drug validation apparatus 100 may generate a second artificial intelligence learning model through repeated learning.

According to one disclosure, the second artificial intelligence learning model is learned to derive another treatment method when the disease is not treated due to the first drug, and the expression state of the target gene according to the change in the component and content of the first drug It is obtained based on the result of learning the relationship between the change and the change in the expression state of the target gene according to the change in the component and content of the second drug.

According to one disclosure, the second artificial intelligence learning model extracts the pathway in which the first drug acts on the target gene, and according to the change in the content of the first drug, the activation and inhibition reactions of other genes included in the pathway ( inhibition), and by analyzing the activation response and inhibition response of other genes included in the pathway according to the change in the content of the second drug, a new treatment method for patients included in the second category is determined. Here, the new treatment method includes a change in the content of the first drug, administration of a new drug, the number of drug administrations, and the like.

The second artificial intelligence learning model examines a tissue sample from a patient diagnosed with a disease and compares the state of the protein of the target gene of the first category to AGER, THBS2, CA3, MMP12, MMP-1, MMP-7, MMP-9, MMP-13, MMP-8, MMP-10, MMP-2, PIGR, DCN, PGAM1, CD36, FABP, ACP5, CCDC80, PPBP, LYVE1, STC1, SPON1, At least one selected from IL17RC, MMP1, CA1, SERPINC1, TPSB2, CKB/CKBM, NAMPT/PBEF, PPBP/CTAPIII, F9, DCTPP1, F5, SPOCK2, CAT, PF4, MDK, BGN, CKM, POSTN, PGLYRP1 and CXCL12 can analyze the altered expression state of the protein of The protein listed above by way of one disclosure may be a protein that is a target of a drug.

In addition, according to the fold difference of the at least one protein having the altered expression state of the analyzed protein, the content of the first drug may be increased or decreased, or the administration of the at least one or more other drugs may be determined.

4 is a diagram for explaining a pathway until a drug acts on a gene according to one disclosure.

According to one disclosure, the drug validation device 100 extracts a path in which a drug acts on a target gene using a second artificial intelligence learning model first, and other genes included in the path according to a change in the content of the first drug By analyzing the activation and inhibition of the second drug, and analyzing the activation and inhibition responses of other genes included in the pathway according to the change in the content of the second drug, new treatment can be decided.

According to one disclosure, the drug validation apparatus 100 may re-create a new drug using a biological network including a plurality of genetic information, and the biological network is an interaction between biological entities such as genes and proteins ( It means a network made up of interactions).

Referring to FIG. 4(A), the right end of each pathway connecting between the action gene, the intermediate gene, and the disease gene is indicated by an arrow or a circle, where the arrow means activation, and the circle is means inhibition.

A relationship between the drug 401 and the disease gene or target disease may be derived based on the shortest path derived according to the disclosure and whether activity/inhibition, and furthermore, the relationship between the drug 401 and the disease gene or target disease A step of calculating a score by digitizing is performed.

Such a score can be calculated based on the number of intermediate genes arranged on the shortest path, the activity/inhibition of the shortest path, the relationship between the drug and the acting gene, and the disease state of the disease gene.

Referring to (B) of FIG. 4 , the disease gene may be two of the first disease gene 403 and the second disease gene 404, which means that the drug 401 to be verified is two disease genes ( 404, 405).

On the other hand, the input drug 401 may act to treat a disease by reacting with a disease gene, or may act to aggravate the disease. ) acts to treat a disease by reacting with the first disease gene 404 that is suppressed in the disease state, and inhibits the second disease gene 405, which is activated in the disease state, to treat the disease do.

Extracting the shortest path between the effector genes 402 and 403 and the disease genes 404 and 405 extracts all the shortest paths that can connect between the effector genes 402 and 403 and the disease genes 404 and 405, and extracting the shortest path between the effector genes (402, 403) and the disease genes (404, 405) based on the number of at least one intermediate gene arranged on the pathway.

5 is a diagram for explaining the flow of a method of verifying the effectiveness of a drug according to one disclosure and recommending another drug.

The drug effectiveness verification device 100 of the present invention may determine the effectiveness of the drug, and determine the effectiveness of the method of administration,

First, the drug effectiveness verification apparatus 100 injects a first drug to patients suffering from the same disease, patients who are cured by the first drug into a first category, and patients who are not cured by the second category can be classified as

The drug effectiveness verification device 100 analyzes the expression state of the target gene of each of the patients included in the first category according to the component ratio, one dose, the number of doses, the dose time, and the dosing method of the first drug, 1 The efficacy value of a drug can be calculated. In addition, the drug effectiveness verification apparatus 100 may determine a method for taking the first drug customized for each of the patients included in the first category according to the utility value of the first drug.

As a result, the drug validation device 100 is cured of the disease by using the same first drug, but it is possible to determine a dosing method that can effectively use the first drug by considering the gene expression state of the patient more precisely.

In addition, the drug validation device 100 may check the disease treatment status of the patients included in the second category after the administration of the new drug by the second artificial intelligence learning model. In addition, the drug effectiveness verification device 100 by the second artificial intelligence learning model re-learned by reflecting the disease treatment status, the component and content of the first drug, the component and content of the second drug, and the component and content of other drugs may be recrystallized to determine a treatment drug for the second category of patients. That is, the drug efficacy verification apparatus 100 may provide a more accurate customized dosing method by reflecting the feedback from the patient and re-learning based on the feedback.

The drug validation apparatus 100 analyzes the expression state of the target gene of each of the patients included in the second category according to the combination of the first drug and the second drug, and the synergistic value of the combination of the first drug and the second drug can be calculated.

In addition, the drug effectiveness verification device 100 determines a method for custom taking the first drug and the second drug for each of the patients included in the second category according to the synergistic value of the combination of the first drug and the second drug can

As used herein, the term cell refers to a mammalian cell, preferably a human cell. A single cell may be used in accordance with the teachings of the present invention as well as a plurality of cells. Preferably the plurality of cells comprises at least 10 and no more than 500 cells. The cells are preferably present in a single suspension so that the number of cells can be counted even if adherent cells and aggregates are still detected. The plurality of cells can be obtained from any biological sample, such as cell lines, primary cultures, and cell samples (eg, biopsies (surgical biopsies including incisional or excisional biopsies, fine needle aspiration biopsies, etc.), complete excisions or body fluids). Biopsy screening methods are known in the art.

Cells in a biological sample can be assayed for functional enzymes without any pretreatment. Accordingly, it is preferred that the cells in the biological sample are intact cells (ie, whole cells), and cells capable of growing. However, it is appreciated that pretreatment of cells, such as cell extracts or generation of non-infectious cells, is also contemplated by the present invention.

Diseases that can be detected by the method of the present invention include cancer, and cancer is expressed by cancer cells, and non-limiting examples of cancer cells include adenocarcinoma cells, adrenal gland tumor cells, enamel epithelium. ameloblastoma cells, anaplastic cells, anaplastic carcinoma of thyroid cells, angiofibroma cells, angioma cells, angiosarcoma cells, apudoma cells (apudoma cell), argentaffinoma cell, arrhenoblastoma cell, ascites tumor cell, ascites tumor cell, astroblastoma cell, astroblastoma cell astrocytoma cells, ataxia-telangiectasia cells, atrial myxoma cells, basal cell carcinoma cells, benign tumor cells, osteosarcoma cells (bone cancer cell), bone tumor cell (bone tumor cell), brainstem glioma cell (brainstem glioma cell), brain tumor cell (brain tumor cell), breast cancer cell (breast cancer cell), Burkitt's lymphoma cell (Burkitt's lymphoma cell), Cancerous cells, carcinoid cells, carcinoma cells, cerebellar astrocytoma cells, cervical cancer cells, cherry angioma cells , Cholangiocarcinoma cells, cholangioma cells, chondroblastoma cells, chondroma cells, chondrosarcoma cells, chorioblastoma cells, chorioblastoma cells ( choriocarcinoma cells, colon cancer cells, common acute lymphoblastic leukemia cells, craniopharyngioma cells, cystocarcinoma cells, cystofbroma cells cell), cystoma cell, cytoma cell, non-invasive ductal carcinoma in situ cell, ductal papilloma cell, dysgerminoma cell, brain tumor encephaloma cells, endometrial carcinoma cells, endothelioma cells, ependymoma cells, epithelioma cells, erythroleukemia cells, Ewing's sarcoma Ewing's sarcoma cells, extra nodal lymphoma cells, feline sarcoma cells, fibro adenoma cells, fibro sarcoma cells, follicular adenocarcinoma cells of the thyroid gland (follicular cancer of the thyroid cell), ganglioglioma cells, gastrinoma cells, glioblastoma multiforme cells (g) lioblastoma multiform cell, glioma cell, gonadoblastoma cell, haemangioblastoma cell, haemangioendothelioblastoma cell, haemangioendothelioma cell, perivascular haemangiopericytoma cells, haematolymphangioma cells, haemocytoblastoma cells, haemocytoma cells, hairy cell leukemia cells, hamartoma cells, hepatocarcinoma cells, hepatocellular carcinoma cells, hepatoma cells, histoma cells, Hodgkin's disease cells, adrenal glands Hypernephroma cells, infiltrating cancer cells, infiltrating ductal cell carcinoma cells, insulinoma cells, juvenile angioforoma cells, Kaposi's sarcoma Kaposi sarcoma cells, kidney tumor cells, large cell lymphoma cells, leukemia cells, chronic leukemia cells, acute leukemia cells ), lipoma cells, liver cancer cells, liver metastases cell), Luce carcinoma cells, lymphadenoma cells, lymphangioma cells, lymphocytic leukemia cells, lymphocytic lymphoma cells, lymphocytic Lymphoeytoma cells, lymphoedema cells, lymphoma cells, lung cancer cells, malignant mesothelioma cells, malignant teratoma cells, Mastocytoma cells, medulloblastome cells, melanoma cells, meningioma cells, mesothelioma cells, metastatic cells, metastatic cells ( metastasis cells, metastatic spread cells, Morton's neuroma cells, multiple myeloma cells, myeloblastoma cells, myeloid leukemia cells, bone marrow Myelolipoma cells, myeloma cells, myoblastoma cells, myxoma cells, nasopharyngeal carcinoma cells, neoplastic cells, nephroblastoma cells cell), neuroblastoma cells, neurofibroma cells, neurofibromatosis cells, Neuroglioma cells, neuroma cells, non-Hodgkin's lymphoma cells, oligodendroglioma cells, optic glioma cells, osteochondroma cells ( osteochondroma cells, osteosarcoma cells, osteosarcoma cells, ovarian cancer cells, Paget's disease of the nipple cell, pancoast tumor cell), pancreatic cancer cells, phaeochromocytoma cells, pheoehromocytoma cells, plasmacytoma cells, primary brain tumor cells, fetal potential Progonoma cells, prolactinoma cells, renal cell carcinoma cells, retinoblastoma cells, rhabdomyosarcoma cells, rhabdosarcoma cells , solid tumor cells, sarcoma cells, secondary tumor cells, seminoma cells, skin cancer cells, small cell carcinoma cells ), squamous cell carcinoma cell, strawberry haemangioma cell, T-cell lymphoma cell, teratoma teratoma cells, testicular cancer cells, thymoma cells, trophoblastic tumor cells, tumorigenic cells, tumor initiation cells , tumor progression cells, vestibular schwannoma cells, Wilm's tumor cells, or a combination thereof.

6 is a view for explaining the configuration of a device for verifying the effectiveness of a drug acting on a target gene using artificial intelligence according to the disclosure.

According to one disclosure, the drug validation device 100 may include a processor 1300 and a memory 1100, but as shown in FIG. 6, it may be implemented by more components than essential components.

For example, as shown in FIG. 6 , the drug validation device 100 according to an embodiment includes a memory 1100 , a display unit 1210 , a camera 1610 and a processor 1300 , and an output unit ( 1200 ), a communication unit 1500 , a sensing unit 1400 , an A/V input unit 1600 , and a user input unit 1700 may be further included.

The memory 1100 may store a program for processing and control of the processor 1300 , and may store an image input to the drug validation device 100 or guide information output from the drug validation device 100 . . Also, the memory 1100 may store specific information for determining whether guide information is output.

The memory 1100 may include a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg, SD or XD memory), and a RAM. (RAM, Random Access Memory) SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk , may include at least one type of storage medium among optical disks.

Programs stored in the memory 1100 may be classified into a plurality of modules according to their functions, for example, may be classified into a UI module 1110 , a touch screen module 1120 , a notification module 1130 , etc. .

The UI module 1110 may provide a specialized UI, GUI, etc. that are interlocked with the drug effectiveness verification apparatus 100 for each application. The touch screen module 1120 may detect a touch gesture on the user's touch screen and transmit information about the touch gesture to the processor 1300 . The touch screen module 1120 according to an embodiment may recognize and analyze a touch code. The touch screen module 1120 may be configured as separate hardware including a controller.

The notification module 1130 may generate a signal for notifying the occurrence of an event of the drug validation apparatus 100 . Examples of events occurring in the drug validation device 100 include call signal reception, message reception, key signal input, schedule notification, and the like. The notification module 1130 may output a notification signal in the form of a video signal through the display unit 1210 , may output a notification signal in the form of an audio signal through the sound output unit 1220 , and the vibration motor 1230 . It is also possible to output a notification signal in the form of a vibration signal through For example, the notification module 1130 may generate a signal for outputting guide information based on the estimated lane information.

The output unit 1200 may output an audio signal, a video signal, or a vibration signal, and the output unit 1200 may include a display unit 1210 , a sound output unit 1220 , and a vibration motor 1230 . have.

The display unit 1210 displays and outputs information processed by the drug validation apparatus 100 . Specifically, the display unit 1210 may output an image captured by the camera 1610 . Also, the display unit 1210 may synthesize and output the guide information generated by the processor 1300 with the photographed image.

Also, in response to a user input, the display unit 1210 may display a user interface for executing an operation related to the response.

The sound output unit 1220 outputs audio data received from the communication unit 1500 or stored in the memory 1100 . In addition, the sound output unit 1220 outputs a sound signal related to a function (eg, a call signal reception sound, a message reception sound, a notification sound) performed by the drug validation apparatus 100 . For example, the sound output unit 1220 may output guide information generated as a signal from the notification module 1130 as a sound signal under the control of the processor 1300 .

The processor 1300 generally controls the overall operation of the drug validation apparatus 100 . For example, the processor 1300 executes programs stored in the memory 1100 , and thus the user input unit 1700 , the output unit 1200 , the sensing unit 1400 , the communication unit 1500 , and the A/V input unit 1700 . ) can be controlled in general. In addition, the processor 1300 may perform the function of the drug efficacy verification apparatus 100 by executing the programs stored in the memory 1100 .

The sensing unit 1400 may detect a state of the drug validation device 100 or a state around the drug validation device 100 , and transmit the sensed information to the processor 1300 .

The sensing unit 1400 includes a magnetic sensor 1410 , an acceleration sensor 1420 , a temperature/humidity sensor 1430 , an infrared sensor 1440 , a gyroscope sensor 1450 , and a position sensor. (eg, GPS) 1460 , a barometric pressure sensor 1470 , a proximity sensor 1480 , and at least one of an RGB sensor 1490 , but is not limited thereto. Since a function of each sensor can be intuitively inferred from the name of a person skilled in the art, detailed description thereof will be omitted.

According to an embodiment, the sensing unit 1400 may measure a distance between the vehicle and at least one object determined in the captured image.

The communication unit 1500 may include one or more components that allow the drug validation device 100 to communicate with another device (not shown) and a server (not shown). The other device (not shown) may be a computing device such as the drug validation device 100 or a sensing device, but is not limited thereto. For example, the communication unit 1500 may include a short-range communication unit 1510 , a mobile communication unit 1520 , and a broadcast receiving unit 1530 .

Short-range wireless communication unit 1510, Bluetooth communication unit, BLE (Bluetooth Low Energy) communication unit, short-range wireless communication unit (Near Field Communication unit), WLAN (Wi-Fi) communication unit, Zigbee (Zigbee) communication unit, infrared ( It may include an IrDA, infrared Data Association) communication unit, a Wi-Fi Direct (WFD) communication unit, an ultra wideband (UWB) communication unit, an Ant+ communication unit, and the like, but is not limited thereto.

The mobile communication unit 1520 transmits/receives a radio signal to and from at least one of a base station, an external terminal, and a server on a mobile communication network. Here, the wireless signal may include various types of data according to transmission/reception of a voice call signal, a video call signal, or a text/multimedia message.

The broadcast receiver 1530 receives a broadcast signal and/or broadcast-related information from the outside through a broadcast channel. The broadcast channel may include a satellite channel and a terrestrial channel. According to an embodiment, the drug validation apparatus 100 may not include the broadcast receiver 1530 .

The A/V (Audio/Video) input unit 1600 is for inputting an audio signal or a video signal, and may include a camera 1610 , a microphone 1620 , and the like. The camera 1610 may obtain an image frame such as a still image or a moving image through an image sensor in a video call mode or a shooting mode. The image captured through the image sensor may be processed through the processor 1300 or a separate image processing unit (not shown).

The microphone 1620 receives an external sound signal and processes it as electrical voice data. For example, the microphone 1620 may receive an acoustic signal from an external device or a user. The microphone 1620 may receive a user's voice input. The microphone 1620 may use various noise removal algorithms for removing noise generated in the process of receiving an external sound signal.

The user input unit 1700 means a means for the user to input data for controlling the drug validation apparatus 100 . For example, the user input unit 1700 includes a key pad, a dome switch, and a touch pad (contact capacitive method, pressure resistance film method, infrared sensing method, surface ultrasonic conduction method, integral type). There may be a tension measurement method, a piezo effect method, etc.), a jog wheel, a jog switch, and the like, but is not limited thereto.

7 is a view for explaining the configuration of a processor that generates an artificial intelligence learning model through data learning according to one disclosure.

The processor 1300 according to an embodiment may include a data learning unit 1310 and a data recognition unit 1320 .

The data learning unit 1310 may learn the expression state of a gene according to the action of the drug. The data learning unit 1310 may learn a criterion regarding which data to use and whether to expand the relation graph using the data in order to increase the accuracy of the relation graph. The data learning unit 1310 may learn a criterion for forming an appropriate relational network according to learning of the relation graph by acquiring data to be used for learning and applying the acquired data to a data recognition model to be described later.

According to an embodiment, when there are a plurality of pre-built data recognition models, the data learning unit 1310 may determine a data recognition model having a high correlation between the input training data and the basic learning data as the data recognition model to be trained. have. In this case, the basic learning data may be pre-classified for each type of data, and the data recognition model may be previously built for each type of data. For example, the basic training data is pre-classified by various criteria such as the region where the training data is generated, the time when the training data is generated, the size of the training data, the genre of the training data, the creator of the training data, the type of object in the training data, etc. may have been

The data recognition unit 1320 may study the expression state of the drug, the target gene, and the expression state of another gene, thereby increasing the accuracy of which drug should be used for each individual to treat a disease. The data recognition unit 1320 may increase the accuracy of recommending a new drug taking method by acquiring predetermined data according to a preset criterion by learning, and using the data recognition model using the acquired data as an input value. . In addition, a result value output by the data recognition model using the obtained data as an input value may be used to update the data recognition model.

At least one of the data learning unit 1310 and the data recognition unit 1320 may be manufactured in the form of at least one hardware chip and mounted on a device. For example, at least one of the data learning unit 1310 and the data recognition unit 1320 may be manufactured in the form of a dedicated hardware chip for artificial intelligence (AI), or a conventional general-purpose processor (eg, CPU) Alternatively, it may be manufactured as a part of an application processor) or a graphics-only processor (eg, GPU) and mounted on the various devices described above.

In this case, the data learning unit 1310 and the data recognition unit 1320 may be mounted on one device or may be mounted on separate devices, respectively. For example, one of the data learning unit 1310 and the data recognition unit 1320 may be included in the device, and the other may be included in the server. In addition, the data learning unit 1310 and the data recognition unit 1320 may provide the model information built by the data learning unit 1310 to the data recognition unit 1320 through a wired or wireless connection, and the data recognition unit ( Data input to 1320 may be provided to the data learning unit 1310 as additional learning data.

Meanwhile, at least one of the data learning unit 1310 and the data recognition unit 1320 may be implemented as a software module. When at least one of the data learning unit 1310 and the data recognition unit 1320 is implemented as a software module (or a program module including instructions), the software module is a computer-readable non-transitory It may be stored in a readable recording medium (non-transitory computer readable media). Also, in this case, at least one software module may be provided by an operating system (OS) or may be provided by a predetermined application. Alternatively, a part of the at least one software module may be provided by an operating system (OS), and the other part may be provided by a predetermined application.

Also, the data recognition unit 1320 may learn a data recognition model used for situation determination by using the training data. In this case, the data recognition model may be a pre-built model. For example, the data recognition model may be a model built in advance by receiving basic training data (eg, a sample image, etc.).

The data recognition model may be constructed in consideration of the field of application of the recognition model, the purpose of learning, or the computer performance of the device. The data recognition model may be, for example, a model based on a neural network. For example, a model such as a deep neural network (DNN), a recurrent neural network (RNN), or a bidirectional recurrent deep neural network (BRDNN) may be used as the data recognition model, but is not limited thereto.

According to various embodiments, when there are a plurality of pre-built data recognition models, the data learning unit 1310 may determine a data recognition model having a high correlation between the input training data and the basic learning data as the data recognition model to be learned. have. In this case, the basic learning data may be pre-classified for each type of data, and the data recognition model may be previously built for each type of data. For example, the basic training data is pre-classified by various criteria such as the region where the training data is generated, the time when the training data is generated, the size of the training data, the genre of the training data, the creator of the training data, the type of object in the training data, etc. may have been

An embodiment may also be implemented in the form of a recording medium containing instructions executable by a computer, such as a program module executed by a computer. Computer-readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. Also, computer-readable media may include both computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Communication media typically includes computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, or other transport mechanism, and includes any information delivery media.

Also, in this specification, "unit" may be a hardware component such as a processor or circuit, and/or a software component executed by a hardware component such as a processor.

The above description of the present invention is for illustration, and those of ordinary skill in the art to which the present invention pertains can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. For example, each component described as a single type may be implemented in a distributed manner, and likewise components described as distributed may be implemented in a combined form.

The scope of the present invention is indicated by the following claims rather than the above detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention. do.

Claims (10)

In the method of verifying the effectiveness of a drug acting on a target gene using artificial intelligence,
Periodically acquiring medical data of patients who received a first drug prepared to cure a disease;
classifying, among the medical data, medical data of patients whose disease was cured after administration of the first drug into a first category, and medical data of patients whose disease was not cured after administration of the first drug, into a second category;
Deriving the target gene from the genes of patients included in the first category using the first artificial intelligence learning model that learned the correlation between the gene expression distribution by the disease and the gene expression distribution by the component of the first drug step;
Obtaining a biomarker profile by analyzing the expression state of the target gene and the expression state of other genes by the first drug from the gene expression information of the patients included in the second category; And
Using a second artificial intelligence learning model that learned the relationship between the target gene and the component and content of the first drug based on the biomarker profile, it can be used to treat diseases of patients included in the second category. Comprising the step of determining whether to administer the second drug and the content of the second drug,
The method of verifying the efficacy of the drug,
By analyzing the expression state of the target gene of each of the patients included in the first category according to the component ratio, one dose, number of doses, dose time, and dosing method of the first drug, the utility value of the first drug is calculated to do; and
Further comprising; according to the utility value of the first drug, determining a customized dosing method of the first drug for each of the patients included in the first category;
The method of verifying the efficacy of the drug,
calculating the synergistic value of the combination of the first drug and the second drug by analyzing the expression state of the target gene of each of the patients included in the second category according to the combination of the first drug and the second drug; and
Further comprising; according to the synergistic value of the combination of the first drug and the second drug, determining a customized dosing method of the first drug and the second drug for each of the patients included in the second category;
The first artificial intelligence learning model,
As learned to derive the target gene of the disease, the change in the expression state of the gene due to the onset of the disease, the cell function, the change in the expression state of the gene due to the first drug, and the change in the expression state of other genes due to the first drug It is obtained based on the results of unsupervised learning of relationships,
The second artificial intelligence learning model,
As learned in order to derive another treatment method when a disease is not treated due to the first drug, the change in the expression state of the target gene according to the change in the component and content of the first drug and the component and content of the second drug It is obtained based on the result of learning the relationship with the change in the expression state of the target gene according to the change of
The second artificial intelligence learning model,
extracting the pathway in which the first drug acts on the target gene, and analyzing the activation and inhibition reactions of other genes included in the pathway according to the change in the content of the first drug, and To determine a new treatment method for patients included in the second category by analyzing the active response and inhibitory response of other genes included in the pathway according to the change in the content of the second drug,
The second artificial intelligence learning model,
AGER, THBS2, CA3, MMP12, MMP-1, MMP in the protein of the target gene of the second category by assaying a tissue sample from a patient diagnosed with the disease and comparing the state of the protein of the target gene of the first category -7, MMP-9, MMP-13, MMP-8, MMP-10, MMP-2, PIGR, DCN, PGAM1, CD36, FABP, ACP5, CCDC80, PPBP, LYVE1, STC1, SPON1, IL17RC, MMP1, CA1 Altered expression of at least one protein selected from , SERPINC1, TPSB2, CKB/CKBM, NAMPT/PBEF, PPBP/CTAPIII, F9, DCTPP1, F5, SPOCK2, CAT, PF4, MDK, BGN, CKM, POSTN, PGLYRP1 and CXCL12 Analyzing the status and determining, according to the fold difference of the at least one protein having the altered expression status, increasing or decreasing the content of the first drug, or determining the dosing of the at least one or more other drugs,
The biomarker profile includes data on genes commonly modified in patients included in the second category,
The first artificial intelligence learning model,
Unsupervised learning is performed by inputting learning data into the input layer of the initial deep neural network, and an intermediate deep neural network with an updated initial connection strength in the initial deep neural network is generated, and the training data is applied to the input layer of the intermediate deep neural network. input to and set the output value specified for the type of training data in the output layer of the intermediate deep neural network to generate a final deep neural network,
The learning data is
A method of verifying the effectiveness of a drug acting on a target gene using artificial intelligence, characterized in that it is expression change gene information for each of a plurality of gene datasets. According to claim 1,
The initial connection strength is
Updated by the Deep Belief Network method,
The deep trust neural network is
A drug acting on a target gene using artificial intelligence, characterized in that the current layer is learned by propagating and backpropagating in the current layer, and the next layer of the current layer is learned when the learning of the current layer is completed How to validate. 3. The method of claim 2,
The type of learning data is,
a first type representing a normal genetic dataset extracted from a normal population, and a second type representing a disease genetic dataset extracted from a patient population;
When the first type of learning data is input to the input layer of the intermediate deep neural network, the output layer of the intermediate deep neural network is set as a first output value,
When the second type of learning data is input to the input layer of the intermediate deep neural network, the output layer of the intermediate deep neural network is set as the second output value, using artificial intelligence to determine the effectiveness of a drug acting on a target gene How to verify. 4. The method of claim 3,
The method of deriving the disease,
obtaining a predicted value by calculating forward propagation in the input layer of the intermediate deep neural network; and
Updating each connection strength of the intermediate deep neural network by backpropagating in a direction to reduce an error between any one of the first output value or the second output value and the predicted value; A method to verify the effectiveness of drugs acting on target genes using 5. The method of claim 4,
The target gene is
MDGA1 (MAM domain containing glycosylphosphatidylinositol anchor 1),
The MDGA1 is,
A method of verifying the effectiveness of a drug acting on a target gene using artificial intelligence, characterized in that it consists of a polynucleotide comprising the base 37617785 to the base 37618223 on chromosome 6 or a polynucleotide complementary thereto. 6. The method of claim 5,
The target gene is
CYP4V2 (cytochrome P450 family 4 subfamily V member 2) further comprising,
The CYP4V2 is characterized in that it consists of a polynucleotide including the 187124479 base to the 187125005 base on chromosome 4 or a complementary polynucleotide thereof,
The target gene is
further comprising at least one of Arf-GAP with dual PH domain-containing protein 1 (ADAP1), LIM homeobox 6 (LHX6) and Ras GTPase-activating protein 3 (RASA3);
The ADAP1 consists of a polynucleotide comprising from the 948157th base to the 949189th base on chromosome 7 or a complementary polynucleotide thereof,
The LHX6 consists of a polynucleotide comprising the 124989781th to the 124989950th base on chromosome 9 or a complementary polynucleotide thereof,
The RASA3 is characterized in that it consists of a polynucleotide comprising the base 114813885 to the base 114814341 on chromosome 13 or a complementary polynucleotide thereof,
The target gene is
further comprising at least one of a polymeric immunoglobulin receptor (PIGR), a disintegrin and metalloproteinase domain-containing protein 12 (ADAM12), and a chromosome 21 open reading frame 128 (C21orf128);
The PIGR consists of a polynucleotide comprising the base 207105468 to the base 207105762 on chromosome 1 or a complementary polynucleotide thereof,
The ADAM12 consists of a polynucleotide comprising the 127822964th to the 127823174th base on chromosome 10 or a complementary polynucleotide thereof,
The C21orf128 is characterized in that it consists of a polynucleotide comprising from the 43528602 base to the 43529012 base on chromosome 21 or a complementary polynucleotide thereof,
The target gene is
GPR35 (G protein-coupled receptor 35) and STON1 (stonin 1) further comprising at least one,
The GPR35 consists of a polynucleotide comprising the 241562679th to the 241562995th base on chromosome 2 or a complementary polynucleotide thereof,
The STON1 is characterized in that it consists of a polynucleotide containing the base 48983601 to the base 48983821 on chromosome 2 or its complementary polynucleotide, using artificial intelligence to verify the effectiveness of a drug acting on a target gene Way. delete delete delete A computer-readable non-transitory recording medium in which a program for implementing the method of any one of claims 1 to 6 is recorded.

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