A functional gene prediction method based on deep learning

Abstract

The present invention relates to the field of gene prediction technology, and specifically discloses a functional gene prediction method based on deep learning, comprising: step one: detecting the physiological characteristics of a target individual to determine whether the target functional gene is expressed and the degree of expression; step two: analyzing the relationship between the environment and the expression of the target functional gene to determine whether changes in the environment affect the expression of the target functional gene; the present invention can reveal how environmental factors affect gene expression by analyzing the relationship between the environment and the expression of the target functional gene, thereby deeply understanding the interaction mechanism between genes and the environment, formulating corresponding prevention strategies, and regulating gene expression by changing environmental factors, thereby improving the accuracy of gene mining and identification, and providing a powerful tool for further in-depth research on the mechanism of action of gene function.

Description

Technical Field

[0001] The present invention relates to the technical field of gene prediction, and in particular to a functional gene prediction method based on deep learning. Background Art

[0002] Functional genes are part of the Human Genome Project (HGP), which aims to clarify the structure, composition, sequence of all nucleotides, location of genes on chromosomes and their functions of the human genome, so as to decipher all human genetic information. Functional genes have multiple functions, including but not limited to supporting the basic structure and performance of life, participating in biological functions, cytological functions and developmental functions, etc. Specifically, the proteins encoded by functional genes play an important role in organisms, such as enzymes, hormones and structural proteins, which directly affect the morphological and physiological characteristics of individual organisms.

[0003] A Chinese invention patent with publication number CN108733981A discloses a method for predicting liver cancer risk from a genetic perspective based on deep learning technology, including: S1, acquisition of training data; S2, training a deep learning model; S3, predicting liver cancer using the trained deep learning model.

[0004] However, many existing technologies focus mainly on the sequence, structure and function of the gene itself when studying gene expression, while ignoring the impact of environmental factors on gene expression, resulting in an incomplete and in-depth understanding of gene function and its relationship with disease. Summary of the invention

[0005] The purpose of the present invention is to provide a functional gene prediction method based on deep learning to solve the technical problems in the above background.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A functional gene prediction method based on deep learning, comprising the following steps:

[0008] Step 1: Detect the physiological characteristics of the target individual to determine whether the target functional gene is expressed and the degree of expression;

[0009] Step 2: Analyze the relationship between the environment and the expression of the target functional gene to determine whether the change in the environment affects the expression of the target functional gene;

[0010] B31: Acquire physiological characteristic parameters SL and environmental parameters HJ of different individuals, and respectively package the physiological characteristic parameters SL and environmental parameters HJ of different individuals into a set of point data (SL, HJ);

[0011] B32: Based on all the point data, a correlation analysis point map is constructed in a two-dimensional coordinate system with the environmental parameter HJ as the X-axis and the physiological characteristic parameter SL as the Y-axis;

[0012] B33: Analyze all the points in the point map based on the correlation, and obtain a fitting straight line and the fitting straight line equation through the least squares method: , where k represents the slope of the fitted line, b represents the intercept of the fitted line, and k and b are constants, y is the ordinate, is the horizontal axis;

[0013] B34: Predict the expression level of target functional genes;

[0014] Substitute the subsequently obtained environmental parameters into the fitting linear equation to calculate the predicted values ​​of the physiological characteristic parameters, thereby predicting the expression level of the target functional gene based on the predicted values ​​of the physiological characteristic parameters.

[0015] As a further solution of the present invention: the method for determining whether the target functional gene is expressed and the degree of expression is:

[0016] A1: Detect the physiological characteristics of the target individual to obtain physiological characteristic data;

[0017] A2: Calculate and obtain physiological characteristic parameters based on individual physiological characteristic data;

[0018] A3: Compare and analyze the physiological characteristic parameters to determine whether the target functional gene is expressed and the degree of expression.

[0019] As a further solution of the present invention: the physiological characteristic data includes: basic physiological characteristic characterization values, metabolic characterization values, and muscle characterization values.

[0020] As a further solution of the present invention: the method for acquiring the physiological characteristic data is:

[0021] A11: Obtain the target basic physiological characteristics of the target individual, and perform product calculation on the values ​​of all the target basic physiological characteristics. The ratio of the value obtained by the product calculation to the basic physiological characteristic threshold is the basic physiological characteristic representation value, and the basic physiological characteristic representation value is marked as JC;

[0022] A12: Obtain the metabolic rate of the target individual through a metabolic rate detection instrument. The ratio of the metabolic rate to the metabolic rate threshold is the metabolic characterization value, and the metabolic characterization value is marked as DX;

[0023] A13: The muscle strength parameters of the target individual are obtained through a muscle strength testing instrument. The ratio of the muscle strength parameters to the muscle threshold is the muscle characterization value, and the muscle characterization value is marked as JR.

[0024] As a further solution of the present invention: the basic physiological characteristic characterization value JC, the metabolic characterization value DX, and the muscle characterization value JR are processed by the formula: , calculate and obtain the physiological characteristic parameter SL, where , , are preset scaling factors, and are all greater than 0.

[0025] As a further solution of the present invention: the physiological characteristic threshold is preset as SY, and the physiological characteristic parameter SL is compared and analyzed with the physiological characteristic threshold SY;

[0026] If the physiological characteristic parameter SL is less than the physiological characteristic threshold SY, it is determined that the target functional gene is not expressed;

[0027] If the physiological characteristic parameter SL ≥ the physiological characteristic threshold SY, it is determined that the target functional gene is expressed, and the larger the value of the physiological characteristic parameter SL, the higher the degree of expression of the target functional gene.

[0028] As a further solution of the present invention: the method for predicting the expression level of the target functional gene is:

[0029] B1: Obtain the environmental data of the target individual;

[0030] B2: Calculate and obtain environmental parameters based on the environmental data of the target individual;

[0031] B3: According to the physiological characteristic parameters and environmental parameters of different individuals, the relationship between the physiological characteristic parameters and the environmental parameters is analyzed, and a model is established to predict the physiological characteristic parameters according to the environmental parameters, thereby predicting the expression level of the target functional gene.

[0032] As a further solution of the present invention: the environmental data includes: nutritional status characterization value, living habit characterization value, and external influence characterization value.

[0033] As a further solution of the present invention: the method for obtaining the environmental data is:

[0034] B11: Obtain the nutritional status of the target individual and score the nutritional status of the target individual from 0 to 10. The higher the score, the better the nutritional status of the target individual. The score is the nutritional status representation value, and the nutritional status representation value is marked as YY;

[0035] B12: Obtain the living habits of the target individual and score the living habits of the target individual from 0 to 10. The higher the score, the better the living habits of the target individual. The score is the living habit representation value, and the living habit representation value is marked as SS;

[0036] B13: Obtain the external environment information of the target individual, and score the impact of the external environment on the target individual from 0 to 10. The higher the score, the greater the positive impact of the external environment on the target individual. The score is the external impact representation value, and the external impact representation value is marked as WJ.

[0037] As a further solution of the present invention: the nutritional status characterization value YY, the living habit characterization value SS, and the external influence characterization value WJ are processed by the formula: , calculate the environmental parameter HJ, where , , are preset ratio parameters, and are all greater than 0.

[0038] Beneficial effects of the present invention:

[0039] (1) By analyzing the relationship between the environment and the expression of target functional genes, the present invention can reveal how environmental factors affect gene expression, thereby deeply understanding the interaction mechanism between genes and the environment, formulating corresponding prevention strategies, and regulating gene expression by changing environmental factors to prevent potential health problems;

[0040] (2) The present invention provides a comprehensive analysis from physiological characteristics to environmental factors, helping to gain a deeper understanding of the expression of target functional genes and their relationship with the environment, thereby predicting the expression of functional genes and providing strong support for the prevention and treatment of health problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] Figure 1 is a flow chart of the method of the present invention;

[0043] Figure 2 It is a flow chart of the method for determining whether the target functional gene is expressed and the degree of expression in the present invention;

[0044] Figure 3 It is a flow chart of the method for predicting the expression level of the target functional gene in the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] Embodiment 1:

[0047] See also Figure 1 - Figure 3 As shown, a functional gene prediction method based on deep learning according to an embodiment of the present invention comprises the following steps:

[0048] Step 1: Detect the physiological characteristics of the target individual to determine whether the target functional gene is expressed and the degree of expression;

[0049] The target individuals are: anyone, including but not limited to healthy people, patients, athletes, soldiers, astronauts and other special occupations, as well as volunteers participating in scientific research or clinical trials;

[0050] A1: Detect the physiological characteristics of the target individual to obtain physiological characteristic data. By collecting the physiological characteristic data of the target individual, we can fully understand their physiological state and provide basic data for subsequent analysis. These data will serve as a direct basis for determining the expression level of the target functional gene, and also help to understand the overall health status of the individual;

[0051] Among them, the physiological characteristic data include: basic physiological characteristic representation value, metabolic representation value, muscle representation value;

[0052] A11: Obtain the target basic physiological characteristics of the target individual, and perform product calculation on the values ​​of all the target basic physiological characteristics. The ratio of the value obtained by the product calculation to the basic physiological characteristic threshold is the basic physiological characteristic representation value, and the basic physiological characteristic representation value is marked as JC;

[0053] Among them, basic physiological characteristics include but are not limited to: body temperature, blood pressure, heart rate, respiratory rate;

[0054] The basic physiological characteristics are digitized to facilitate subsequent data processing and comparative analysis. By multiplying and comparing the product with the threshold, the basic physiological condition of the individual can be quantified, providing an important indicator for evaluating the expression level of functional genes.

[0055] A12: Obtain the metabolic rate of the target individual through a metabolic rate detection instrument. The ratio of the metabolic rate to the metabolic rate threshold is the metabolic characterization value, and the metabolic characterization value is marked as DX;

[0056] Among them, metabolic rate detection instruments include but are not limited to: gas analyzers, bioelectrical impedance meters, metabolic meters;

[0057] Metabolic rate is an important indicator that reflects the physiological state of an individual. It helps to fully understand the individual's metabolic level. The metabolic characterization value can reflect the energy consumption and material conversion efficiency of the target individual and is an important reference for evaluating the expression level of functional genes.

[0058] A13: Obtain the muscle strength parameter of the target individual through a muscle strength detection instrument. The ratio of the muscle strength parameter to the muscle threshold is the muscle representation value, and the muscle representation value is marked as JR;

[0059] Among them, muscle strength testing instruments include but are not limited to: dynamometer, muscle strength tester;

[0060] Muscle strength is an important indicator of individual physical fitness and health status. Muscle characterization value can provide quantitative information about individual muscle strength and help evaluate the impact of functional genes on muscle growth and strength.

[0061] A2: Based on the physiological characteristic data of the individual, the physiological characteristic parameters are calculated and multiple physiological characteristic representation values ​​are integrated into a comprehensive index to facilitate subsequent analysis. The physiological characteristic parameters can fully reflect the physiological state of the individual and provide a comprehensive basis for judging the expression level of functional genes.

[0062] The basic physiological characteristic representation value JC, metabolic representation value DX, and muscle representation value JR are processed by the formula: , calculate and obtain the physiological characteristic parameter SL, where , , are preset scaling factors, and are all greater than 0;

[0063] A3: Compare and analyze physiological characteristic parameters to determine whether the target functional gene is expressed and the degree of expression, provide a clear basis for the regulation and intervention of the target functional gene, and help formulate personalized health management plans;

[0064] The physiological characteristic threshold is preset as SY, and the physiological characteristic parameter SL is compared and analyzed with the physiological characteristic threshold SY. By comparing and analyzing the physiological characteristic parameter with the physiological characteristic threshold, the expression of the functional gene can be directly determined;

[0065] Among them, the value of the physiological characteristic threshold will change according to the difference of the target functional gene, the environment of the target individual, and the developmental stage of the target individual;

[0066] If the physiological characteristic parameter SL is less than the physiological characteristic threshold SY, it is determined that the target functional gene is not expressed;

[0067] If the physiological characteristic parameter SL ≥ the physiological characteristic threshold SY, it is determined that the target functional gene is expressed, and the larger the value of the physiological characteristic parameter SL, the higher the degree of expression of the target functional gene;

[0068] Step 2: Analyze the relationship between the environment and the expression of the target functional gene, so as to predict the expression level of the target functional gene;

[0069] B1: Obtaining the environmental data of the target individual and understanding the environmental conditions of the individual will help analyze the impact of the environment on the expression of functional genes. The environmental data will provide basic data for subsequent environmental parameter calculations and correlation analysis.

[0070] Environmental data include nutritional status characterization values, lifestyle characterization values, and external influence characterization values, which quantify the individual's status in different environments, thus facilitating subsequent analysis and modeling. These characterization values ​​can reflect the comprehensive impact of the environment on the individual's physiological state and functional gene expression, providing a basis for subsequent prediction and intervention.

[0071] B11: Obtain the nutritional status of the target individual and score the nutritional status of the target individual from 0 to 10. The higher the score, the better the nutritional status of the target individual. The score is the nutritional status representation value, and the nutritional status representation value is marked as YY;

[0072] Among them, the scoring criteria for nutritional status are: mainly based on the target individual's nutrient intake, digestion and absorption capacity, and the body's utilization of nutrients, to conduct a comprehensive and detailed assessment;

[0073] B12: Obtain the living habits of the target individual and score the living habits of the target individual from 0 to 10. The higher the score, the better the living habits of the target individual. The score is the living habit representation value, and the living habit representation value is marked as SS;

[0074] Among them, the scoring criteria for living habits are: mainly based on the target individual's daily routine, exercise frequency and intensity, dietary regularity and quality, stress management ability, etc., to conduct a comprehensive and detailed assessment;

[0075] B13: Obtain the external environment information of the target individual, and score the impact of the external environment on the target individual from 0 to 10. The higher the score, the greater the positive impact of the external environment on the target individual. The score is the external impact representation value, and the external impact representation value is marked as WJ;

[0076] Among them, the scoring criteria for external influences are: mainly considering the physical conditions, social environment, exposure to chemicals or pathogens, and psychological stress of the target individual's environment; physical conditions include air quality, noise level, lighting conditions, etc.; social environment covers social interaction, cultural atmosphere, work and study environment, etc.; exposure to chemicals or pathogens involves environmental pollution, food safety, disease prevalence and other factors; psychological stress considers life rhythm, work pressure, family relationships, etc.;

[0077] B2: Based on the environmental data of the target individual, calculate the environmental parameters and integrate multiple environmental characterization values ​​into a comprehensive indicator to facilitate subsequent analysis and modeling. The environmental parameters can fully reflect the environmental conditions of the individual and provide a comprehensive basis for analyzing the relationship between the environment and the expression of functional genes;

[0078] The nutritional status representation value YY, the living habit representation value SS, and the external influence representation value WJ are processed by the formula: , calculate the environmental parameter HJ, where , , are preset ratio parameters, and are all greater than 0;

[0079] B3: Based on the physiological characteristic parameters and environmental parameters of different individuals, the correlation between physiological characteristic parameters and environmental parameters is analyzed, and a model is established to predict the physiological characteristic parameters based on the environmental parameters, thereby predicting the expression level of the target functional gene. Through modeling and correlation analysis, the relationship between physiological characteristic parameters and environmental parameters can be revealed, providing a scientific basis for predicting the expression level of functional genes, helping to understand how environmental factors affect the expression of functional genes, and providing guidance for formulating targeted intervention measures;

[0080] B31: Acquire physiological characteristic parameters SL and environmental parameters HJ of different individuals, and respectively package the physiological characteristic parameters SL and environmental parameters HJ of different individuals into a set of point data (SL, HJ);

[0081] B32: Based on all the point data, a correlation analysis point map is constructed in a two-dimensional coordinate system with the environmental parameter HJ as the X-axis and the physiological characteristic parameter SL as the Y-axis;

[0082] B33: Analyze all the points in the point map based on the correlation, and obtain a fitting straight line and the fitting straight line equation through the least squares method: y , where k represents the slope of the fitted line, b represents the intercept of the fitted line, and k and b are constants, y is the ordinate, is the horizontal axis;

[0083] B34: Predict the expression level of target functional genes;

[0084] Substitute the subsequently obtained environmental parameters into the fitting linear equation to calculate the predicted values ​​of the physiological characteristic parameters, thereby predicting the expression level of the target functional gene based on the predicted values ​​of the physiological characteristic parameters.

[0085] Embodiment 2:

[0086] Based on the embodiment, a functional gene prediction system based on deep learning described in the embodiment of the present invention includes:

[0087] The physiological characteristics analysis module is used to detect the physiological characteristics of the target individual, so as to determine whether the target functional gene is expressed and the degree of expression;

[0088] The environmental factor analysis module is used to analyze the relationship between the environment and the expression of the target functional gene, so as to predict the expression level of the target functional gene;

[0089] Among them, the physiological characteristic analysis module includes: a physiological characteristic data acquisition unit, a physiological characteristic data processing unit, and a physiological characteristic parameter analysis unit;

[0090] The physiological characteristic data acquisition unit is used to detect the physiological characteristics of the target individual, thereby obtaining physiological characteristic data;

[0091] The physiological characteristic data processing unit is used to calculate and obtain physiological characteristic parameters according to the physiological characteristic data of the individual;

[0092] The physiological characteristic parameter analysis unit is used to compare and analyze the physiological characteristic parameters, so as to determine whether the target functional gene is expressed and the degree of expression;

[0093] The environmental factor analysis module includes: an environmental data acquisition unit, an environmental data collection unit, and an environmental parameter analysis unit;

[0094] The environmental data collection unit obtains the environmental data of the target individual;

[0095] The environmental data processing unit calculates and obtains environmental parameters according to the environmental data of the target individual;

[0096] The environmental parameter analysis unit analyzes the relationship between the physiological characteristic parameters and the environmental parameters according to the physiological characteristic parameters of different individuals and the environmental parameters, and establishes a model to predict the physiological characteristic parameters according to the environmental parameters, thereby predicting the expression level of the target functional gene.

[0097] The above thresholds are set for the convenience of comparison. The thresholds depend on the amount of sample data and the number of bases set by technicians in this field for each set of sample data.

[0098] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The factors in the formula are set by technicians in this field according to actual conditions; for example: Formula ; A technician in this field collects multiple sets of physiological characteristic data and sets corresponding physiological characteristic parameters for each set of physiological characteristic data; substitutes the set physiological characteristic parameters and the collected physiological characteristic data into the formula, any three formulas constitute a three-variable linear equation system, and the calculated factors are screened and averaged to obtain , , The values ​​of are 2.13, 2.58 and 1.93 respectively;

[0099] The size of the factor is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the factor depends on the amount of physiological characteristic data and the preliminary setting of the corresponding physiological characteristic parameters for each set of physiological characteristic data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the physiological characteristic parameter is proportional to the metabolic characterization value.

[0100] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A functional gene prediction method based on deep learning, characterized in that: The following steps are involved: Step 1: Detect the physiological characteristics of the target individual to determine whether the target functional gene is expressed and the degree of expression; Step 2: Analyze the relationship between the environment and the expression of the target functional gene, so as to predict the expression level of the target functional gene and determine whether the change in the environment affects the expression of the target functional gene; In step 2, the method for predicting the expression level of the target functional gene is: B1: Obtain the environmental data of the target individual; B11: Obtain the nutritional status of the target individual and score the nutritional status of the target individual from 0 to 10. The higher the score, the better the nutritional status of the target individual. The score is the nutritional status representation value, and the nutritional status representation value is marked as YY; Among them, the scoring criteria for nutritional status are: mainly based on the target individual's nutrient intake, digestion and absorption capacity, and the body's utilization of nutrients, to conduct a comprehensive and detailed assessment; B12: Obtain the living habits of the target individual and score the living habits of the target individual from 0 to 10. The higher the score, the better the living habits of the target individual. The score is the living habit representation value, and the living habit representation value is marked as SS; Among them, the scoring criteria for living habits are: mainly based on the target individual's daily routine, exercise frequency and intensity, dietary regularity and quality, stress management ability, etc., to conduct a comprehensive and detailed assessment; B13: Obtain the external environment information of the target individual, and score the impact of the external environment on the target individual from 0 to 10. The higher the score, the greater the positive impact of the external environment on the target individual. The score is the external impact representation value, and the external impact representation value is marked as WJ; Among them, the scoring criteria for external influences are: mainly considering the physical conditions, social environment, exposure to chemicals or pathogens, and psychological stress of the target individual's environment; physical conditions include air quality, noise level, lighting conditions, etc.; social environment covers social interaction, cultural atmosphere, work and study environment, etc.; exposure to chemicals or pathogens involves environmental pollution, food safety, disease prevalence and other factors; psychological stress considers life rhythm, work pressure, family relationships, etc.; B2: Calculate and obtain environmental parameters based on the environmental data of the target individual; The nutritional status representation value YY, the living habit representation value SS, and the external influence representation value WJ are processed by the formula: , calculate the environmental parameter HJ, where , , are preset ratio parameters, and are all greater than 0; B3: According to the physiological characteristic parameters and environmental parameters of different individuals, the correlation between the physiological characteristic parameters and the environmental parameters is analyzed, and a model is established to predict the physiological characteristic parameters according to the environmental parameters, thereby predicting the expression level of the target functional gene; B31: Acquire physiological characteristic parameters SL and environmental parameters HJ of different individuals, and respectively package the physiological characteristic parameters SL and environmental parameters HJ of different individuals into a set of point data (SL, HJ); B32: Based on all the point data, a correlation analysis point map is constructed in a two-dimensional coordinate system with the environmental parameter HJ as the X-axis and the physiological characteristic parameter SL as the Y-axis; B33: Analyze all the points in the point map based on the correlation, and obtain a fitting straight line and the fitting straight line equation through the least squares method: , where k represents the slope of the fitted line, b represents the intercept of the fitted line, and k and b are constants, y is the ordinate, is the horizontal axis; B34: Predict the expression level of target functional genes; Substitute the subsequently obtained environmental parameters into the fitting linear equation to calculate the predicted values ​​of the physiological characteristic parameters, thereby predicting the expression level of the target functional gene based on the predicted values ​​of the physiological characteristic parameters.

2. A method for predicting functional genes based on deep learning according to claim 1, characterized in that: The method for determining whether the target functional gene is expressed and the degree of expression is as follows: A1: Detect the physiological characteristics of the target individual to obtain physiological characteristic data; A2: Calculate and obtain physiological characteristic parameters based on individual physiological characteristic data; A3: Compare and analyze the physiological characteristic parameters to determine whether the target functional gene is expressed and the degree of expression.

3. A method for predicting functional genes based on deep learning according to claim 2, characterized in that: The physiological characteristic data include: basic physiological characteristic characterization values, metabolic characterization values, and muscle characterization values.

4. A method for predicting functional genes based on deep learning according to claim 3, characterized in that: The method for acquiring the physiological characteristic data is: A11: Obtain the target basic physiological characteristics of the target individual, and perform product calculation on the values ​​of all the target basic physiological characteristics. The ratio of the value obtained by the product calculation to the basic physiological characteristic threshold is the basic physiological characteristic representation value, and the basic physiological characteristic representation value is marked as JC; A12: Obtain the metabolic rate of the target individual through a metabolic rate detection instrument. The ratio of the metabolic rate to the metabolic rate threshold is the metabolic characterization value, and the metabolic characterization value is marked as DX; A13: The muscle strength parameters of the target individual are obtained through a muscle strength testing instrument. The ratio of the muscle strength parameters to the muscle threshold is the muscle characterization value, and the muscle characterization value is marked as JR.

5. The method for predicting functional genes based on deep learning according to claim 4, characterized in that: The basic physiological characteristic representation value JC, metabolic representation value DX, and muscle representation value JR are processed by the formula: , calculate and obtain the physiological characteristic parameter SL, where , , are preset scaling factors, and are all greater than 0.

6. The method for predicting functional genes based on deep learning according to claim 5, characterized in that: The physiological characteristic threshold is preset as SY, and the physiological characteristic parameter SL is compared and analyzed with the physiological characteristic threshold SY; If the physiological characteristic parameter SL is less than the physiological characteristic threshold SY, it is determined that the target functional gene is not expressed; If the physiological characteristic parameter SL ≥ the physiological characteristic threshold SY, it is determined that the target functional gene is expressed, and the larger the value of the physiological characteristic parameter SL, the higher the degree of expression of the target functional gene.

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