WO2022022667A1 - Système de prédiction de tolérance à l'alcool fondé sur le criblage de gènes - Google Patents
Système de prédiction de tolérance à l'alcool fondé sur le criblage de gènesDownload PDFInfo
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- WO2022022667A1 WO2022022667A1PCT/CN2021/109457CN2021109457WWO2022022667A1WO 2022022667 A1WO2022022667 A1WO 2022022667A1CN 2021109457 WCN2021109457 WCN 2021109457WWO 2022022667 A1WO2022022667 A1WO 2022022667A1
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- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
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- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
Definitions
- the inventionrelates to the technical field of biological genes, in particular to a system for predicting alcohol consumption based on genetic screening.
- alcoholAfter alcohol enters the human body, it enters the blood circulation directly through the biofilm through the oral cavity, esophagus, stomach, intestine and other organs, and is quickly transported to various tissues and organs of the body for metabolism and utilization.
- Alcohol metabolismis mainly completed by two enzymes (alcohol dehydrogenase and aldehyde dehydrogenase), and the difference in drinking ability (alcohol amount) between individuals is mainly determined by the activities of these two enzymes, and the activity of the enzymes is determined by the gene In the final analysis, the amount of alcohol a person can drink is determined by genetics.
- the present inventionaims to solve one of the technical problems in the above technologies at least to a certain extent.
- the purpose of the present inventionis to propose a drinking quantity prediction system based on genetic screening, which provides a drinking quantity judgment standard, quantifies an individual's drinking ability, and gives a more intuitive and valuable alcohol quantity evaluation and drinking according to the user's physical condition. Recommendations to improve user experience.
- the embodiment of the present inventionproposes a system for predicting alcohol consumption based on genetic screening, including:
- the user gene acquisition moduleis used to obtain the user's genetic data
- the drinking volume prediction model interface moduleis connected with the user gene acquisition module, and inputs the user's genetic data into the drinking volume prediction model to predict the drinking volume of the user, and obtains the first prediction result of the drinking volume;
- a drinking suggestion database moduleconnected with the drinking quantity prediction model interface module, and used for giving drinking suggestions according to the first prediction result of the drinking quantity of the user;
- a query moduleconnected with the interface module of the drinking volume prediction model, and used for querying the relationship between the drinking volume-related genes, the drinking rank and the drinking volume, and the drinking advice corresponding to the drinking tier in the drinking volume prediction model;
- the display moduleis connected with the drinking suggestion database module, and is used for displaying the user's genetic data, the prediction result of drinking amount, and the drinking suggestion.
- the user's genetic datais obtained, and the user's genetic data is input into the alcohol consumption prediction model to predict the user's alcohol consumption, wherein the alcohol consumption prediction model adopts a decision tree classification model Construct, get the first prediction result of drinking amount, give drinking suggestion according to the first prediction result of user's drinking amount through the drinking suggestion database module, and finally display the user's genetic data, the predicted result of drinking amount, and drinking suggestion through the display module , according to the user's physical condition, it will give more intuitive and valuable alcohol evaluation and drinking suggestions to improve the user's experience.
- the query moduleis used to query the relationship between alcohol consumption-related genes, drinking grades and alcohol consumption, and drinking recommendations corresponding to drinking grades based on the alcohol consumption prediction model. related information.
- the user gene acquisition moduleincludes:
- a first obtaining moduleused to obtain the saliva of the user
- a first processing moduleconnected to the first acquisition module, for extracting DNA from the saliva of the user, and performing gene sequencing on the extracted DNA;
- the second processing moduleis connected to the first processing module, and is used to perform bioinformatics analysis after gene sequencing, obtain the genotype of the specified site, and format the genotype data of the specified site rs1229984 and rs671.
- the alcohol consumption prediction modelis constructed by selecting a machine learning model, including:
- the acquiring genetic data of the sample and formatting the genetic dataincludes:
- S23Process the gene data after gene sequencing to obtain the genotype of the locus related to the alcohol consumption of each sample;
- genetic locus screeningis performed on the genetic data of the formatted sample, including:
- the methodfurther includes: a user terminal, connected to the display module, for receiving relevant data information sent by the display module to facilitate viewing by the user.
- a second obtaining moduleconfigured to obtain second information that affects the drinking amount of the user, the second information including: disease history, drinking type, drinking degree, and drinking frequency;
- the calculation moduleis connected with the interface module of the drinking volume prediction model, the second acquisition module, and the drinking recommendation database module, and is used for calculating the drinking volume according to the first prediction result and the second information according to a preset algorithm The second prediction result of ;
- the drinking suggestion database moduleis further configured to give drinking suggestions according to the second prediction result of the drinking amount of the user.
- the preset algorithmincludes:
- V 1A ⁇ c
- Ais the drinking volume (ml) output by the alcohol volume prediction model based on the user's genetic data
- cis the preset alcohol concentration (%vol) in the alcohol volume prediction model
- V 2V 1 ⁇ d ⁇ t ⁇ f
- dis the correlation coefficient between the user's disease history and drinking amount
- tis the correlation coefficient between drinking type and drinking amount
- fis the correlation coefficient between drinking frequency and drinking amount
- cuis the drinking degree input by the user.
- the genetic screening-based alcohol consumption prediction systemuses SOA system architecture; including:
- the data layeris used to obtain user data according to the data source
- a data platform layerconnected to the data layer, for providing a development environment for data management
- the service layerconnected with the data platform layer, is used for receiving data information sent by the data platform, and providing services for data consumers according to the data information.
- FIG. 1is a block diagram of a genetic screening-based alcohol consumption prediction system according to an embodiment of the present invention
- FIG. 2is a block diagram of a user gene acquisition module according to an embodiment of the present invention.
- FIG. 3is a flowchart of a method for constructing a drinking quantity prediction model according to an embodiment of the present invention
- FIG. 4is a flowchart of processing genetic data of a sample according to an embodiment of the present invention.
- Fig. 5is a flow chart of the screening of genetic loci related to alcohol consumption according to an embodiment of the present invention.
- FIG. 6is a block diagram of a genetic screening-based alcohol consumption prediction system according to yet another embodiment of the present invention.
- FIG. 7is a block diagram of an SOA system architecture according to an embodiment of the present invention.
- FIG. 8is a schematic diagram of a decision tree of drinking quantity-related genes and drinking rank according to an embodiment of the present invention.
- User gene acquisition module 1first acquisition module 11, first processing module 12, second processing module 13, drinking quantity prediction model interface module 2, drinking suggestion database module 3, query module 4, display module 5, user terminal 6,
- the second acquisition module 7the calculation module 8 , the data layer 9 , the data platform layer 10 , and the service layer 14 .
- the followingdescribes a system for predicting the amount of alcohol consumption based on genetic screening proposed in an embodiment of the present invention with reference to FIG. 1 to FIG. 8 .
- Fig. 1is a block diagram of a system for predicting alcohol consumption based on genetic screening according to an embodiment of the present invention; as shown in Fig. 1, an embodiment of the present invention proposes a system for predicting alcohol consumption based on genetic screening, including:
- User gene acquisition module 1used to acquire the user's genetic data
- the drinking volume prediction model interface module 2is connected to the user gene acquisition module 1, and inputs the user's genetic data into the drinking volume prediction model to predict the drinking volume of the user, and obtains the first prediction result of the drinking volume;
- the drinking suggestion database module 3is connected with the drinking quantity prediction model interface module 2, and is used for giving drinking suggestions according to the first prediction result of the drinking quantity of the user;
- a query module 4connected with the drinking volume prediction model interface module 2, is used to query the relationship between the drinking volume-related genes, the drinking rank and the drinking volume, and the drinking advice corresponding to the drinking rank in the drinking volume prediction model;
- the display module 5is connected with the drinking suggestion database module 3, and is used for displaying the user's genetic data, the prediction result of the drinking amount, and the drinking suggestion.
- the user's genetic datais obtained, and the user's genetic data is input into the alcohol consumption prediction model to predict the user's alcohol consumption, wherein the alcohol consumption prediction model adopts a decision tree classification model Construct, get the first prediction result of drinking amount, give drinking suggestion according to the first prediction result of user's drinking amount through the drinking suggestion database module, and finally display the user's genetic data, the predicted result of drinking amount, and drinking suggestion through the display module , according to the user's physical condition, it will give more intuitive and valuable alcohol evaluation and drinking suggestions to improve the user's experience.
- the query moduleis used to query the relationship between alcohol consumption-related genes, drinking grades and alcohol consumption, and drinking recommendations corresponding to drinking grades based on the alcohol consumption prediction model. related information.
- Fig. 2is a block diagram of a user gene acquisition module according to an embodiment of the present invention. As shown in Fig. 2, the user gene acquisition module 1 includes:
- the first obtaining module 11is used to obtain the saliva of the user
- the first processing module 12connected with the first acquisition module 11, is used to extract DNA from the saliva of the user, and perform gene sequencing on the extracted DNA;
- the second processing module 13is connected to the first processing module 12, and is used to perform bioinformatics analysis after gene sequencing, obtain the genotype of the specified site, and format the genotype data of the specified site rs1229984 and rs671 .
- DNA extraction and chip sequencingare performed according to the saliva of the user, the rs1229984 gene locus and the rs671 gene locus of the user are found out, and the genotype data of the designated loci rs1229984 and rs671 are formatted and processed , to obtain the genetic data related to the user's alcohol consumption, which is convenient and fast.
- Fig. 3is a flow chart of a method for constructing an alcohol consumption prediction model according to an embodiment of the present invention. As shown in Fig. 3, the alcohol consumption prediction model is constructed by selecting a machine learning model, including:
- the working principle and beneficial effects of the above technical solutionsobtain the relationship between the drinking ability and the drinking amount of the sample through a questionnaire survey method, and carry out data analysis, divide the drinking amount into a first preset number of drinking grades, and establish a first database. Dividing alcohol consumption into drinking grades and quantifying alcohol consumption can help to give more valuable drinking advice.
- the genetic data of the sampleis formatted, and the alcohol consumption prediction model is constructed according to the genetic data of the formatted sample and the first database.
- FIG. 4is a flowchart of processing genetic data of a sample according to an embodiment of the present invention. As shown in FIG. 4 , the acquiring and formatting the genetic data of the sample includes:
- S23Process the gene data after gene sequencing to obtain the genotype of the locus related to the alcohol consumption of each sample;
- DNA extraction, gene sequencing, and genotypingare performed on the saliva of the sample to obtain the genetic data of the sample; the gene sequencing methods include: chip sequencing, second-generation sequencing, third-generation sequencing, PCR sequencing, At least one of panel sequencing.
- the genotypes of the loci related to the alcohol consumption of each sampleare obtained.
- the lociare formatted into numbers according to the genotype. Illustratively, wild type is 0, heterozygous mutant is 1, and homozygous mutant is 2.
- CCis a homozygous mutant, formatted as a number 2;
- TTis a wild type, formatted as a number 0;
- CTis a heterozygous mutant, formatted as a number 1; such as at the rs671 locus
- AAis a homozygous mutant, formatted as a number 2;
- GGis a wild type, formatted as a number 0;
- AGis a heterozygous mutant, formatted as a number 1.
- Fig. 5is a flow chart of the screening of gene loci related to alcohol consumption according to an embodiment of the present invention. As shown in Fig. 5, the gene locus screening is performed on the gene data of the formatted sample, including:
- the method for measuring the purity and uncertainty of the data set before and after dividing the data setincludes calculating at least one parameter in the information gain, the information gain rate, and the Gini coefficient. Specifically, the method is determined according to the Gini coefficient. In the method of purity and uncertainty, the larger the Gini coefficient, the higher the uncertainty of the data, and the lower the sample purity, indicating that the proportion of the target sample in the data set to the total sample is smaller; the smaller the Gini coefficient, the uncertainty of the data.
- the Gini coefficientis equal to 0, all samples in the dataset are of the same class.
- the result of the rs671 locus of the sampleis GG, that is, it is determined whether the format of the rs671 locus of the sample is 0, and according to whether the rs671 locus is 0, the first
- the databaseis divided into two data sets, the first data set and the second data set, wherein the rs671 gene locus of the samples in the first data set is GG, and the rs671 gene locus results of the samples in the second data set are AA and AG; Calculate the Gini coefficient of the eigenvalues of each gene locus in the first data set and the second data set, and select the gene locus A with the smallest calculated Gini coefficient and the eigenvalue a of the gene locus A, wherein the all The gene locus A is used as a child node, and the data set is split again according to the grouping of the characteristic value a of the gene locus A; for example, in the first data set, it is judged
- the drinking segmentis 8 segments.
- each groupis a sample of the same type, that is, the Gini coefficient is 0.
- the splittingis stopped, and the gene loci related to the amount of drinking and the relationship between the gene locus and the drinking rank are finally obtained.
- the genes related to alcohol consumptionare divided into corresponding drinking grades according to their gene types, which is convenient for memory, and can accurately reflect the corresponding relationship between gene types and alcohol consumption, which is clear at a glance and improves user experience.
- the loci related to alcohol consumptioninclude the rs1229984 locus and the rs671 locus, wherein the rs1229984 locus is located on the ADH1B gene, and when the result of the rs1229984 locus is TT type, The activity of alcohol dehydrogenase is strong, and the metabolism of ethanol is fast; the result is CT type, the activity of alcohol dehydrogenase is moderate, and the rate of alcohol metabolism is moderate; the result is that the activity of alcohol dehydrogenase is weak, and the rate of alcohol metabolism is slow; the rs671 gene locus is located in In the ALDH2 gene, when the rs671 gene locus was GG type, the activity of acetaldehyde dehydrogenase was strong, and the acetaldehyde metabolism was fast; when the result was GA ⁇ AA type, the activity of acetaldehyde dehydrogenase was weak, and the metabolism of acetalde
- a decision tree classification modelis selected to construct a drinking quantity prediction model.
- a decision tree classification modelwas used to construct a drinking consumption prediction model
- Algorithmsinclude:
- DecisionTreeClassifier modulemain parameter settings:
- criterion'gini': select the Gini coefficient as the metric for the quality of node division;
- splitter'best': find the best split point among all features
- max_depthNone: Set the maximum depth of the decision tree, None means that the maximum depth of the decision tree is not constrained until the samples on each leaf node belong to the same class;
- min_samples_split2: When dividing an internal node, the minimum number of samples on the node is required to be 2;
- min_samples_leaf1: Set the minimum number of samples on the leaf node to 1;
- the first preset numberis 7, and the drinking rank is 7 ranks, and the relationship between the rs1229984 locus and the rs671 locus and the drinking amount is obtained as shown in Table 2.
- the working principle and beneficial effects of the above technical solutionswhen the drinking rank is 0, there are three situations: 1.
- the rs1229984 locusis CC, and the rs671 locus is AA; 2.
- the rs1229984 locusis TT and the rs671 locus 3.
- the rs1229984 locusis CT, and the rs671 locus is AA.
- the naming of drinking tiersis done in a discontinuous way. If 3 and 6 tiers are missing, the discontinuous naming can match the drinking tier with the specific amount of alcohol consumed. For example, when the drinking tier is 9, the user The amount of alcohol consumed is 9 taels or more.
- Fig. 6is a block diagram of a system for predicting alcohol consumption based on genetic screening according to yet another embodiment of the present invention; as shown in Fig. 6 , it further includes: a user terminal 6, connected to the display module, for receiving relevant information sent by the display module Data information is convenient for users to view.
- a second obtaining module 7configured to obtain second information that affects the drinking amount of the user, where the second information includes: disease history, drinking type, drinking degree, and drinking frequency;
- the calculation module 8is connected with the interface module of the drinking quantity prediction model, the second acquisition module, and the drinking suggestion database module, and is used for calculating the drinking alcohol according to the first prediction result and the second information according to a preset algorithm. the second prediction result of the quantity;
- the drinking suggestion database module 3is further configured to give drinking suggestions according to the second prediction result of the drinking amount of the user.
- the first prediction result of the drinking amount output by the drinking amount prediction model based on the user's genetic datais combined with the second information that affects the drinking amount of the user to modify the prediction of the drinking amount,
- the second informationincludes: disease history, drinking type, drinking degree, drinking frequency; for example, as shown in Table 2, if the user's genetic data is rs1229984 locus CC, rs671 locus is GG, then the user's drinking The volume is predicted to be 7 segments, that is, the user can drink more than 7 taels of wine (take 50° liquor as an example), but the user has recently suffered from stomach problems and cannot drink alcohol. Drinking alcohol can easily cause gastric perforation, which seriously endangers health.
- the prediction of the user's drinking amountwill also be affected.
- the second prediction result of drinking amountis calculated and obtained, which can make more effective drinking amount prediction according to the actual situation of the user, and the prediction result is more accurate.
- the preset algorithmincludes:
- V 1A ⁇ c
- Ais the drinking volume (ml) output by the alcohol volume prediction model based on the user's genetic data
- cis the preset alcohol concentration (%vol) in the alcohol volume prediction model
- V 2V 1 ⁇ d ⁇ t ⁇ f
- dis the correlation coefficient between the user's disease history and drinking amount
- tis the correlation coefficient between drinking type and drinking amount
- fis the correlation coefficient between drinking frequency and drinking amount
- cuis the drinking degree input by the user.
- the correlation coefficient d between the user's disease history and alcohol consumptionis 0, that is, the user cannot drink alcohol; other users' disease history
- the value of the correlation coefficient d with the amount of drinkingis between 0 and 1; the value of the correlation coefficient t between the type of drinking and the amount of drinking is shown in Table 3; the value of the correlation coefficient f between the frequency of drinking and the amount of drinking is shown in Table 4.
- the first prediction result of drinking quantityis revised, and the second prediction result of drinking quantity is calculated, which can make more effective prediction of drinking quantity according to the actual situation of the user, the prediction result is more accurate, and the most correct user is given. drinking recommendations to improve user experience.
- FIG. 8is a block diagram of an SOA system architecture according to an embodiment of the present invention. As shown in FIG. 8 , the genetic screening-based alcohol consumption prediction system uses the SOA system architecture; including:
- the data layer 9is used to obtain user data according to the data source
- a data platform layer 10, connected to the data layer 9,is used to provide a development environment for data management;
- the service layer 14, connected to the data platform layer 10,is used for receiving data information sent by the data platform, and providing services for data consumers according to the data information.
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| CN112102920B (zh)* | 2020-07-30 | 2023-11-10 | 苏州因顿医学检验实验室有限公司 | 一种基于基因筛选的饮酒量预测系统 |
| CN114908146A (zh)* | 2022-05-31 | 2022-08-16 | 因顿健康科技(苏州)有限公司 | 一种快速基因检测判断酒量的方法 |
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