A rapid diagnosis system for animal multiple birth genes based on biotechnology

Abstract

The invention discloses a rapid diagnosis system for animal multiple birth genes based on biotechnology, comprising a sample collection module, a gene extraction module, a gene sequencing module, a gene information model construction module, a rapid diagnosis module, a technology update and maintenance module, and a result presentation and report module. The sample collection module is used to obtain biological data, the gene extraction module is used to extract gene data, the gene sequencing module is used to sequence gene data, the gene information model construction module is used to mine biological information in genes, the rapid diagnosis module is used for rapid multiple birth gene diagnosis, the technology update and maintenance module is used for system update and maintenance, and the result presentation and report module is used to present diagnosis results. The invention discloses a rapid diagnosis system for animal multiple birth genes based on biotechnology, proposes a data mining algorithm based on genome association analysis to mine and analyze animal gene information, and proposes a rapid diagnosis algorithm for multiple birth genes based on variance modeling analysis to diagnose multiple birth genes.

Description

Technical Field

[0001] The present invention relates to the technical fields of data mining, machine learning and variance analysis, and in particular to a rapid animal multiple birth gene diagnosis system based on biotechnology. Background Art

[0002] Genetic data mining technology is a data mining method based on biotechnology, which aims to solve the complexity of animal multiple birth genetic diagnosis. By mining the data mining algorithm of genome association analysis, it can effectively extract potential gene associations and biological information from gene sequencing data, and provide accurate genetic information for the rapid diagnosis module. Through machine learning technology, it builds a highly personalized gene information model to provide the system with in-depth analysis and decision support for multiple birth genes, ensuring real-time and accurate genetic diagnosis decisions in a virtual environment, enhancing the practicality of the overall system for animal multiple birth genetic diagnosis, and making the animal multiple birth genetic rapid diagnosis system based on biotechnology more reliable and scientific.

[0003] Variance analysis technology is a data analysis method based on statistical methods. It aims to identify differences in genetic data. By analyzing the variance of gene expression data, the average differences between different groups are compared. By analyzing the variance of the data, the differences between groups can be effectively evaluated, and the correlations in animal multiple birth genes can be effectively distinguished. It provides data support for rapid and accurate multiple birth gene diagnosis. Variance analysis technology performs differential analysis and pattern recognition on genetic data. Through statistical methods, key information in genetic data is extracted to ensure that the system can perform more accurate and detailed analysis and diagnosis in animal multiple birth gene diagnosis. Summary of the invention

[0004] The purpose of the present invention is to provide a biotechnology-based animal multiple birth gene rapid diagnosis system to solve the problems of low accuracy in extracting potential gene associations and biological information from gene sequencing data, the complexity of making real-time and accurate gene diagnosis decisions in a virtual environment, and the problem of effectively identifying differences in animal multiple birth gene data proposed in the above-mentioned background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a biotechnology-based rapid diagnosis system for animal multiple birth genes, comprising a sample collection module, a gene extraction module, a gene sequencing module, a gene information model construction module, a rapid diagnosis module, a technology update and maintenance module, and a result presentation and reporting module, characterized in that: the sample collection module is used to obtain necessary biological data from animal multiple birth biological samples as a starting point for subsequent gene sequencing and diagnosis, the gene extraction module is used to extract high-quality gene data from the collected samples to provide reliable basic data for subsequent gene sequencing, the gene sequencing module is used to sequence animal gene data, obtain detailed information on animal multiple birth genes, and provide original gene data for subsequent data analysis and diagnosis, and the gene information model construction module includes gene information mining. The gene information mining unit proposes a data mining algorithm based on genome association analysis for animal gene data mining and constructs a personalized gene information model. The gene information analysis unit deeply analyzes the multiple birth gene information of different types of animals according to the constructed personalized gene information model. The rapid diagnosis module proposes a multiple birth gene rapid diagnosis algorithm based on variance modeling analysis, which can quickly and accurately screen out genetic information related to animal multiple births from large-scale gene data and provide rapid diagnosis results. The technology update and maintenance module is used for system update and maintenance to ensure continuous optimization of the system and maintain its cutting-edge position in the field of biotechnology. The result presentation and reporting module is used to present the results of the rapid diagnosis module in an intuitive and concise form, and provide a diagnosis report to facilitate user understanding and application.

[0006] Preferably, the sample collection module obtains biological data from the biological sample accurately and losslessly, thereby ensuring that the collected biological data remains in its original state, and providing reliable and accurate genetic data for subsequent genetic analysis.

[0007] Preferably, the gene extraction module ensures that high-quality gene data is obtained from the collected samples through an efficient gene extraction method, providing a reliable basis for subsequent sequencing and analysis to ensure the accuracy of subsequent gene sequencing.

[0008] Preferably, the gene sequencing module ensures that detailed information on animal multiple birth genes, including genetic variation factors, is obtained by accurately sequencing gene data, thereby providing accurate and comprehensive gene data for subsequent data analysis.

[0009] Preferably, the gene information model construction module includes a gene information mining unit, which proposes a data mining algorithm based on genome association analysis, deeply mines potential gene associations in biological samples, and constructs a comprehensive gene information model to discover potential genetic interactions and correlations.

[0010] Specifically, the data mining algorithm based on genome association analysis is as follows: First, through the microarray gene significance analysis method, the variance stabilization factor e0 is introduced to reduce the variability of variance within different intensity ranges and improve the effect of unstable error estimation in gene two-sided test. By considering the non-uniformity of gene expression levels, the relative difference is calculated as:

[0011]

[0012] Among them, R(i) represents the ratio of the expression change of the gene in two states to the standard deviation of the data, x I (i) and x U (i) represents the average expression value of gene i in state I and state U, respectively, e(i) represents the standard pooled deviation of the expression value of gene i in the two states, λ represents the parameter for adjusting the variance stabilization factor, Var(·) represents the calculation of the variance, and the variance of the difference between gene expression values ​​is introduced to better adapt to the non-uniformity of gene expression levels and improve the modeling accuracy of gene-specific fluctuations; secondly, through the statistical method of gene expression level difference detection, the statistic for gene expression level difference detection is calculated as:

[0013]

[0014] Where H represents the ratio of the median difference of genes under two comparison conditions to the summary standard error of the benchmark variance estimated by the detection of gene expression level differences, m1 and m2 represent the median of the gene data under the two comparison conditions, and H p It is represented as the summary standard error of the benchmark variance estimated for gene expression level difference detection, and α represents the parameter used to adjust the statistic for gene expression level difference detection. The variance of the difference between the gene medians is introduced to enhance the sensitivity of the gene expression level difference detection technology to gene differences in small sample conditions. Then, the association analysis between gene expressions is performed using the comprehensive statistical information of the microarray gene significance analysis method and the statistical method for gene expression level difference detection to extract biologically meaningful association and difference information. In order to deal with the complex comparison of multiple biological conditions and pathways, a variance analysis model is established to capture multiple effects. The model formula is:

[0015] y nmki =A n +D m +V k +τ+log{1+exp[G(i)]}+θ nmki

[0016] Among them, y nmki represents the expression level of gene i under specific conditions, A n Indicates the degree of influence of different arrays on gene expression, Dm Indicates the degree of influence of different dyes on gene expression, V k represents the influence of different sample pathways on gene expression, τ represents the average expression level of multiple genes, G(i) represents the expression level of gene i under the current conditions, and G(i) is logarithmically transformed to capture the nonlinear relationship of gene expression, θ nmki represents the error term of gene expression caused by other factors not considered by the model, which obeys an independent and identically distributed normal distribution. n represents the sample array index to which the gene expression data belongs, m represents the dye index to which the gene expression data belongs, and k represents the sample pathway index to which the gene expression data belongs. Finally, a heterogeneous error model is constructed to solve the problem of inaccurate estimation in the case of small samples. The error pooling prior is constructed using a statistical method based on Bayesian hierarchical modeling and gene expression level difference detection. The total error variation is decomposed into two levels: technical and biological. The first and second levels are:

[0017] y nmki =x nmk +θ nmki

[0018] x nmk =μ+g n +c m +r nm +b nmk

[0019] Among them, x nmk represents the true expression level of gene i under the nth array, mth dye and kth sample pathway, μ represents the average expression level of the overall observations, and g n Indicates the influence level of gene on the observed value, c m The differences in gene expression are related to different cell types, r nm Indicates the expression difference caused by the change of specific environmental conditions, b nmk It represents the biological error of gene i under the same array, dye and sample pathway. The first layer is designed to capture the array technical variation caused by the experimental error component. The second layer is designed to capture the biological error with different variability for each different combination of genes and conditions. Through the biological technology of microarray gene significance analysis method and statistical method for detecting differences in gene expression levels, combined with genome association analysis and statistical modeling, the algorithm comprehensively mines the correlation and difference of gene expression in microarray data, providing a more comprehensive analysis method for in-depth understanding of gene function and the overall gene information of biological processes.

[0020] Preferably, the gene information model construction module includes a gene information analysis unit, which performs an in-depth analysis of the variation of multiple-birth genes based on the constructed personalized gene information model to identify genetic variations related to the multiple-birth state and provide the system with more accurate and diverse multiple-birth gene information.

[0021] Preferably, the rapid diagnosis module proposes a rapid diagnosis algorithm for multiple births genes based on variance modeling analysis, which ensures that the system can provide rapid and accurate diagnosis results in a short time by quickly screening out genetic information related to animal multiple births from large-scale genetic data.

[0022] Specifically, the multiple birth gene rapid diagnosis algorithm based on variance modeling analysis is as follows: First, a portion of important gene data subsets are selected, and the association between multiple birth genes and phenotypes is found in the selected subsets. Assume that I n represents the genotype of the nth multiple-birth gene, n represents the index of the number of genotypes of the multiple-birth gene, J = {j1, j2, ..., j m} represents the observed phenotypic data, j m represents the phenotypic value of the mth animal individual, where m is the index of the number of phenotypic data. For any multiple birth gene I n , the F statistic corresponding to a single multiple birth gene between multiple birth genes and phenotypic data J is calculated by the variance analysis test statistic of multiple birth genes:

[0023]

[0024] Among them, F(I n , J) indicates a single multiple birth gene I n The ANOVA F statistic, E B (I n , J) indicates multiple birth gene I n The between-group sum of squares in the total sum of squares for phenotype J, E T (I n , J) indicates multiple birth gene I n The total sum of squares for phenotype J, P represents the number of animals, Expressed as the degrees of freedom between groups, for each pair of multiple birth genes (I n , I l ), where l represents the index of the number of genotypes of the multiple-birth gene. The F statistic corresponding to the multiple-birth gene pair between the multiple-birth gene pair and the phenotype data J is calculated to measure their association with the phenotype J. The formula is:

[0025]

[0026] Among them, F(I n , I l, J) indicates two polygynous genes I n and I l The ANOVA F statistic, E B (I n , I l , J) indicates two polygynous genes I n and I l The sum of squares between groups is used to measure the proportion of inter-group variation in the total variation between multiple birth genes and phenotype J, E T (I n , I l , J) indicates two polygynous genes I n and I l The total sum of squares between λ and λ represents the number of genotype categories of the multiple birth gene; secondly, by introducing the upper limit value U of the error term H with U K For E B (I n , I l , J) is updated and restricted, and the update formula is:

[0027] E B (I n , I l , J) = E B (I n , J)+U H +U K

[0028] Among them, for the difference term U between group H and other groups in the variance analysis test H The calculation formula is:

[0029]

[0030] where x H represents the number of individuals in the genome H, and h is represented by n When the value is 1, l A subgroup formed by the genotype combination, x h represents the number of individuals in the gene subgroup h, T H It is expressed as the sum of the phenotypic values ​​of individuals in group H, l h Indicates T H The lower bound of is:

[0031]

[0032] u h Indicates T H The upper bound of is:

[0033]

[0034] represents the phenotypic value of the i-th individual in group H, i represents the phenotypic data index of the individual in group H, and for the difference term U between group K and other groups in the variance analysis test K The calculation formula is:

[0035]

[0036] where x K represents the number of individuals in genome K, k represents the number of individuals in I n When the value is 1, l Another subgroup formed by the genotype combination, T K It is expressed as the sum of the phenotypic values ​​of individuals in group K, l k Indicates T K The lower bound of is:

[0037]

[0038] u k Indicates T K The upper bound of is:

[0039]

[0040] represents the phenotypic value of the i-th individual in group K, i represents the phenotypic data index of the individual in group K, for each pair of multiple birth genes (I n , I l ), calculate the F statistic of the multiple birth gene, and use the upper limit value for truncation, then perform a permutation-based test to obtain the maximum multiple birth gene F statistic for each permutation; finally, to resolve the complex nonlinear relationship between genes, a genomic nonlinear relationship model was constructed to more comprehensively capture the association between multiple birth genes. For each pair of multiple birth genes (I n , I l ), through the kernel function Kernel(I n , I l ) measures the nonlinear relationship between multiple gene pairs, and combines the kernel function Kernel(I n , I l ) and the F statistic of the multiple birth gene to obtain the statistical significance of the genetic association:

[0041]

[0042] Where N represents the total number of genotypes of the multiple birth gene, J random represents a random permutation of J. The reference distribution is generated through multiple permutations. According to the permutation test results, the significance threshold α is determined. The gene pairs were considered significant and had potential association with polygynous genes.

[0043] Preferably, the technology update and maintenance module ensures that the system can cope with the evolving biotechnology field by regularly updating the system's gene analysis algorithms and biotechnology methods, while performing maintenance to maintain the stability and reliability of the system.

[0044] Preferably, the result presentation and reporting module ensures that the diagnostic results are presented in a friendly form by utilizing intuitive graphical information, and provides detailed reports, so that users can intuitively understand the diagnostic conclusions of the system in order to better apply the results.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The gene information mining unit proposes a data mining algorithm based on genome association analysis. This algorithm makes full use of high-throughput biotechnology to obtain a large amount of gene expression data, such as animal gene expression microarrays, and conducts in-depth analysis of large-scale genome data to form an information-rich genome data set. In terms of difference analysis, the algorithm adopts a differential expression analysis method, which compares the expression pattern of each gene in pairs. These comparisons are usually calculated by comparing the mean expression values ​​to measure the differences of genes under different environmental conditions. In order to solve the problem that the judgment based on fold changes in traditional methods will lead to misjudgment and the problem that genes with small variance cannot be accurately detected, the algorithm introduces statistical significance and hypothesis testing methods, and pays special attention to the reliability of variance estimation and the problem of multiple comparisons. By adopting statistical methods, such as the t-test method, to test the differential expression of each gene, in the case of only a small number of replications, in order to solve the problem of the lack of reliability of variance estimation in traditional methods, the algorithm adopts Bayesian models for analysis, such as Bayesian parameter modeling, Bayesian regularized t-test, and Bayesian hierarchical modeling methods with multivariate normal priors. These methods help to more accurately estimate the variance of gene expression and improve the accuracy of detecting differences. In general, the data mining algorithm model based on genome association analysis can more accurately and reliably capture the differences in gene expression patterns through efficient analysis of large-scale genome data, combined with difference comparison and statistical methods, and provides an efficient and accurate gene data mining solution for the animal multiple birth gene rapid diagnosis system based on biotechnology;

[0047] 2. Rapid diagnosis module proposes a rapid diagnosis algorithm for multiple births based on variance modeling analysis. This algorithm conducts association analysis on single nucleotide polymorphisms in the genome and uses variance analysis test methods, especially considering the joint genetic effects between multiple single nucleotide polymorphisms. The core principle of this algorithm is to quickly and accurately identify important single nucleotide polymorphism pairs related to animal multiple births through efficient data processing methods. First, the algorithm introduces fast variance analysis, an efficient variance analysis test algorithm, which is used to detect the association between two single nucleotide polymorphisms in large-scale genetic data. Fast variance analysis effectively separates a large number of single nucleotide polymorphism pairs by establishing an upper bound, reducing the computational cost and making it more efficient to find single nucleotide polymorphism pairs related to animal multiple births. Secondly, the algorithm takes into account the variance modeling of genetic information. Analysis, that is, through the variance analysis test of the single nucleotide polymorphism pairs in the genome, an upper bound estimate based on variance is established. This method allows the algorithm to perform detailed variance analysis tests on only a small number of candidate single nucleotide polymorphism pairs without missing any significant single nucleotide polymorphism pairs, thereby reducing the complexity of the calculation. Finally, the algorithm provides an effective update mechanism, which reduces the computational burden by updating the upper bound of the single nucleotide polymorphism test value based on variance analysis in real time. This mechanism enables the algorithm to adapt to large-scale genetic data more efficiently and ensures the rapid diagnosis of animal multiple birth genes in a remote environment. In general, the algorithm not only improves the calculation speed of the system, but also maintains the accuracy of the diagnosis results, providing reliable and efficient multiple birth gene diagnosis results for the animal multiple birth gene rapid diagnosis system based on biotechnology. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The invention is further illustrated by the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0049] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0050] 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.

[0051] See also Figure 1The present invention provides a rapid diagnosis system for animal multiple birth genes based on biotechnology, including a sample collection module, a gene extraction module, a gene sequencing module, a gene information model construction module, a rapid diagnosis module, a technology update and maintenance module, and a result presentation and reporting module. The sample collection module is used to obtain necessary biological data from animal multiple birth biological samples as a starting point for subsequent gene sequencing and diagnosis. The gene extraction module is used to extract high-quality gene data from the collected samples to provide reliable basic data for subsequent gene sequencing. The gene sequencing module is used to sequence animal gene data to obtain detailed information on animal multiple birth genes and provide original gene data for subsequent data analysis and diagnosis. The gene information model construction module includes a gene information mining unit and a gene information The analysis unit and the gene information mining unit propose a data mining algorithm based on genome association analysis for animal gene data mining and construct a personalized gene information model. The gene information analysis unit deeply analyzes the multiple birth gene information of different types of animals based on the constructed personalized gene information model. The rapid diagnosis module proposes a multiple birth gene rapid diagnosis algorithm based on variance modeling analysis, which can quickly and accurately screen out genetic information related to animal multiple births from large-scale gene data and provide rapid diagnosis results. The technology update and maintenance module is used for system update and maintenance to ensure continuous optimization of the system and maintain its cutting-edge position in the field of biotechnology. The result presentation and report module is used to present the results of the rapid diagnosis module in an intuitive and concise form, and provide a diagnosis report to facilitate user understanding and application.

[0052] See also Figure 1 ,Furthermore, the sample collection module accurately and non-destructively obtains biological data from biological samples, ,ensuring that the collected biological data remains in its original state, and providing ,reliable and accurate genetic data for subsequent genetic analysis.

[0053] See also Figure 1 Furthermore, the gene extraction module ensures that high-quality gene data is obtained from the collected samples through an efficient gene extraction method, providing a reliable basis for subsequent sequencing and analysis to ensure the accuracy of subsequent gene sequencing.

[0054] See also Figure 1 ,Furthermore, the gene sequencing module ensures the acquisition of detailed information on animal multiple birth genes, ,including genetic variation factors, by accurately sequencing the gene data, ,providing accurate and comprehensive gene data for subsequent data analysis.

[0055] See also Figure 1 ,Furthermore, the gene information model construction module includes a gene information mining unit, ,which proposes a data mining algorithm based on genome association analysis, deeply ,mines potential gene association relationships in biological samples, and constructs a comprehensive gene information ,model to discover potential genetic interactions and correlations.

[0056] See also Figure 1 ,Furthermore, the data mining algorithm based on genome association analysis is as follows: First, through the microarray gene significance analysis method, the variance stabilization factor e0 is introduced to reduce the variability of variance within different intensity ranges and improve the effect of unstable error estimation in gene two-sided test. By considering the non-uniformity of gene expression levels, the relative difference is calculated as:

[0057]

[0058] Among them, R(i) represents the ratio of the expression change of the gene in two states to the standard deviation of the data, x I (i) and x U (i) represents the average expression value of gene i in state I and state U, respectively, e(i) represents the standard pooled deviation of the expression value of gene i in the two states, λ represents the parameter for adjusting the variance stabilization factor, Var(·) represents the calculation of the variance, and the variance of the difference between gene expression values ​​is introduced to better adapt to the non-uniformity of gene expression levels and improve the modeling accuracy of gene-specific fluctuations; secondly, through the statistical method of gene expression level difference detection, the statistic for gene expression level difference detection is calculated as:

[0059]

[0060] Where H represents the ratio of the median difference of genes under two comparison conditions to the summary standard error of the benchmark variance estimated by the detection of gene expression level differences, m1 and m2 represent the median of the gene data under the two comparison conditions, and H p It is represented as the summary standard error of the benchmark variance estimated for gene expression level difference detection, and α represents the parameter used to adjust the statistic for gene expression level difference detection. The variance of the difference between the gene medians is introduced to enhance the sensitivity of the gene expression level difference detection technology to gene differences in small sample conditions. Then, the association analysis between gene expressions is performed using the comprehensive statistical information of the microarray gene significance analysis method and the statistical method for gene expression level difference detection to extract biologically meaningful association and difference information. In order to deal with the complex comparison of multiple biological conditions and pathways, a variance analysis model is established to capture multiple effects. The model formula is:

[0061] y nmki =A n +D m +V k +τ+log{1+exp[G(i)]}+θ nmki

[0062] Among them, y nmkirepresents the expression level of gene i under specific conditions, A n Indicates the degree of influence of different arrays on gene expression, D m Indicates the degree of influence of different dyes on gene expression, V k represents the influence of different sample pathways on gene expression, τ represents the average expression level of multiple genes, G(i) represents the expression level of gene i under the current conditions, and G(i) is logarithmically transformed to capture the nonlinear relationship of gene expression, θ nmki represents the error term of gene expression caused by other factors not considered by the model, which obeys an independent and identically distributed normal distribution. n represents the sample array index to which the gene expression data belongs, m represents the dye index to which the gene expression data belongs, and k represents the sample pathway index to which the gene expression data belongs. Finally, a heterogeneous error model is constructed to solve the problem of inaccurate estimation in the case of small samples. The error pooling prior is constructed using a statistical method based on Bayesian hierarchical modeling and gene expression level difference detection. The total error variation is decomposed into two levels: technical and biological. The first and second levels are:

[0063] y nmki =x nmk +θ nmki

[0064] x nmk =μ+g n +c m +r nm +b nmk

[0065] Among them, x nmk represents the true expression level of gene i under the nth array, mth dye and kth sample pathway, μ represents the average expression level of the overall observations, and g n Indicates the influence level of gene on the observed value, c m The differences in gene expression are related to different cell types, r nm Indicates the expression difference caused by the change of specific environmental conditions, b nmk It represents the biological error of gene i under the same array, dye and sample pathway. The first layer is designed to capture the array technical variation caused by the experimental error component. The second layer is designed to capture the biological error with different variability for each different combination of genes and conditions. Through the biological technology of microarray gene significance analysis method and statistical method for detecting differences in gene expression levels, combined with genome association analysis and statistical modeling, the algorithm comprehensively mines the correlation and difference of gene expression in microarray data, providing a more comprehensive analysis method for in-depth understanding of gene function and the overall gene information of biological processes.

[0066] See also Figure 1Furthermore, the gene information model construction module includes a gene information analysis unit, which performs in-depth analysis on the variation of multiple birth genes based on the constructed personalized gene information model to identify genetic variations related to the multiple birth state and provide the system with more accurate and diverse multiple birth gene information.

[0067] See also Figure 1 Furthermore, the rapid diagnosis module proposes a rapid diagnosis algorithm for multiple births based on variance modeling analysis, which ensures that the system can provide fast and accurate diagnosis results in a short time by quickly screening out genetic information related to animal multiple births from large-scale genetic data.

[0068] See also Figure 1 Furthermore, the multiple birth gene rapid diagnosis algorithm based on variance modeling analysis is as follows: First, a portion of important gene data subsets are selected, and the association between multiple birth genes and phenotypes is found in the selected subsets. Assume that I n represents the genotype of the nth multiple-birth gene, n represents the index of the number of genotypes of the multiple-birth gene, J = {j1, j2, ..., j m} represents the observed phenotypic data, j m represents the phenotypic value of the mth animal individual, where m is the index of the number of phenotypic data. For any multiple birth gene I n , the F statistic corresponding to a single multiple birth gene between multiple birth genes and phenotypic data J is calculated by the variance analysis test statistic of multiple birth genes:

[0069]

[0070] Among them, F(I n , J) indicates a single multiple birth gene I n The ANOVA F statistic, E B (I n , J) indicates multiple birth gene I n The between-group sum of squares in the total sum of squares for phenotype J, E T (I n , J) indicates multiple birth gene I n The total sum of squares for phenotype J, P represents the number of animals, Expressed as the degrees of freedom between groups, for each pair of multiple birth genes (I n , I l ), where l represents the index of the number of genotypes of the multiple-birth gene. The F statistic corresponding to the multiple-birth gene pair between the multiple-birth gene pair and the phenotype data J is calculated to measure their association with the phenotype J. The formula is:

[0071]

[0072] Among them, F(I n , I l , J) indicates two polygynous genes I n and I l The ANOVA F statistic, E B (I n , I l , J) indicates two polygynous genes I n and I l The sum of squares between groups is used to measure the proportion of inter-group variation in the total variation between multiple birth genes and phenotype J, E T (I n , I l , J) indicates two polygynous genes I n and I l The total sum of squares between λ and λ represents the number of genotype categories of the multiple birth gene; secondly, by introducing the upper limit value U of the error term H with U K For E B (I n , I l , J) is updated and restricted, and the update formula is:

[0073] E B (I n , I l , J) = E B (I n , J)+U H +U K

[0074] Among them, for the difference term U between group H and other groups in the variance analysis test H The calculation formula is:

[0075]

[0076] where x H represents the number of individuals in the genome H, and h is represented by n When the value is 1, l A subgroup formed by the genotype combination, x h represents the number of individuals in the gene subgroup h, T H It is expressed as the sum of the phenotypic values ​​of individuals in group H, l h Indicates T H The lower bound of is:

[0077]

[0078] u h Indicates T H The upper bound of is:

[0079]

[0080] represents the phenotypic value of the i-th individual in group H, i represents the phenotypic data index of the individual in group H, and for the difference term U between group K and other groups in the variance analysis test K The calculation formula is:

[0081]

[0082] where x K represents the number of individuals in genome K, k represents the number of individuals in I n When the value is 1, l Another subgroup formed by the genotype combination, T K It is expressed as the sum of the phenotypic values ​​of individuals in group K, l k Indicates T K The lower bound of is:

[0083]

[0084] u k Indicates T K The upper bound of is:

[0085]

[0086] represents the phenotypic value of the i-th individual in group K, i represents the phenotypic data index of the individual in group K, for each pair of multiple birth genes (I n , I l ), calculate the F statistic of the multiple birth gene, and use the upper limit value for truncation, then perform a permutation-based test to obtain the maximum multiple birth gene F statistic for each permutation; finally, to resolve the complex nonlinear relationship between genes, a genomic nonlinear relationship model was constructed to more comprehensively capture the association between multiple birth genes. For each pair of multiple birth genes (I n , I l ), through the kernel function Kernel(I n , I l ) measures the nonlinear relationship between multiple gene pairs, and combines the kernel function Kernel(I n , I l ) and the F statistic of the multiple birth gene to obtain the statistical significance of the genetic association:

[0087]

[0088] Where N represents the total number of genotypes of the multiple birth gene, J randomrepresents a random permutation of J. The reference distribution is generated through multiple permutations. According to the permutation test results, the significance threshold α is determined. The gene pairs were considered significant and had potential association with polygynous genes.

[0089] See also Figure 1 ,Furthermore, the technology update and maintenance module ensures that the system can cope with the ,evolving biotechnology field by regularly updating the system’s genetic analysis algorithms and biotechnology ,methods, while performing maintenance to maintain the ,stability and reliability of the system.

[0090] See also Figure 1 ,Furthermore, the result presentation and reporting module ensures that the ,diagnosis results are presented in a friendly form by utilizing intuitive ,graphic information and provides detailed reports, so that users can intuitively ,understand the diagnostic conclusions of the system in order to better apply the ,results.

[0091] When used specifically, first, the sample collection module obtains necessary biological data from animal multiple birth biological samples as the starting point for subsequent gene sequencing and diagnosis. Then, the gene extraction module extracts high-quality gene data from the collected samples to provide reliable basic data for subsequent gene sequencing. Subsequently, the gene sequencing module sequences the animal gene data to obtain detailed information on the animal multiple birth genes and provide original gene data for subsequent data analysis and diagnosis. The gene information model construction module includes a gene information mining unit and a gene information analysis unit. The gene information mining unit proposes a data mining algorithm based on genome association analysis to mine animal gene data and construct a personalized gene information model. The gene information analysis unit deeply analyzes the multiple birth gene information of different types of animals based on the constructed personalized gene information model. Secondly, the rapid diagnosis module proposes a rapid diagnosis algorithm for multiple birth genes based on variance modeling analysis to quickly and accurately screen out genetic information related to animal multiple births from large-scale gene data and provide rapid diagnosis results. Finally, the technology update and maintenance module updates and maintains the system to ensure that the system is continuously optimized and remains at the forefront of the field of biotechnology. The result presentation and report module presents the results of the rapid diagnosis module in an intuitive and concise form and provides a diagnosis report to facilitate user understanding and application.

[0092] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A rapid genetic diagnosis system for animal multiple births based on biotechnology, comprising a sample collection module, a gene extraction module, a gene sequencing module, a gene information model construction module, a rapid diagnosis module, a technology update and maintenance module, and a result presentation and reporting module, characterized in that: The sample collection module is used to obtain necessary biological data from animal multiple birth biological samples as a starting point for subsequent gene sequencing and diagnosis. The gene extraction module is used to extract high-quality gene data from the collected samples to provide reliable basic data for subsequent gene sequencing. The gene sequencing module is used to sequence animal gene data, obtain detailed information on animal multiple birth genes, and provide original gene data for subsequent data analysis and diagnosis. The gene information model construction module includes a gene information mining unit and a gene information analysis unit. The gene information mining unit proposes a data mining algorithm based on genome association analysis for animal gene data mining and constructs a personalized gene information model. The gene information analysis unit deeply analyzes the multiple birth gene information of different types of animals based on the constructed personalized gene information model. The rapid diagnosis module proposes a multiple birth gene rapid diagnosis algorithm based on variance modeling analysis, which quickly and accurately screens out genetic information related to animal multiple births from large-scale gene data and provides rapid diagnosis results. The technology update and maintenance module is used for system update and maintenance to ensure continuous optimization of the system and maintain its cutting-edge position in the field of biotechnology. The result presentation and report module is used to present the results of the rapid diagnosis module in an intuitive and concise form, provide a diagnosis report, and facilitate user understanding and application. The gene information model construction module includes a gene information mining unit, which proposes a data mining algorithm based on genome association analysis, deeply mines potential gene association relationships in biological samples, and constructs a comprehensive gene information model to discover potential genetic interactions and correlations; The data mining algorithm based on genome association analysis is as follows: First, through the microarray gene significance analysis method, the variance stabilization factor e0 is introduced to reduce the variability of variance within different intensity ranges and improve the effect of unstable error estimation in gene two-sided test. By considering the non-uniformity of gene expression levels, the relative difference is calculated as: Among them, R(i) represents the ratio of the expression change of the gene in two states to the standard deviation of the data, x I (i) and x U (i) represents the average expression value of gene i in state I and state U, respectively, e(i) represents the standard pooled deviation of the expression value of gene i in the two states, λ represents the parameter for adjusting the variance stabilization factor, Var(·) represents the calculation of the variance, and the variance of the difference between gene expression values ​​is introduced to better adapt to the non-uniformity of gene expression levels and improve the modeling accuracy of gene-specific fluctuations; secondly, through the statistical method of gene expression level difference detection, the statistic for gene expression level difference detection is calculated as: Where H represents the ratio of the median difference of genes under two comparison conditions to the summary standard error of the benchmark variance estimated by the detection of gene expression level differences, m1 and m2 represent the median of the gene data under the two comparison conditions, and H p It is represented as the summary standard error of the benchmark variance estimated for gene expression level difference detection, and α represents the parameter used to adjust the statistic for gene expression level difference detection. The variance of the difference between the gene medians is introduced to enhance the sensitivity of the gene expression level difference detection technology to gene differences in small sample conditions. Then, the association analysis between gene expressions is performed using the comprehensive statistical information of the microarray gene significance analysis method and the statistical method for gene expression level difference detection to extract biologically meaningful association and difference information. In order to deal with the complex comparison of multiple biological conditions and pathways, a variance analysis model is established to capture multiple effects. The model formula is: y nmki (A n +D m +V k +τ+log{1+exp[G(i)]}+θ nmki Among them, y nmki represents the expression level of gene i under specific conditions, A n Indicates the degree of influence of different arrays on gene expression, D m Indicates the degree of influence of different dyes on gene expression, V k represents the influence of different sample pathways on gene expression, τ represents the average expression level of multiple genes, G(i) represents the expression level of gene i under the current conditions, and G(i) is logarithmically transformed to capture the nonlinear relationship of gene expression, θ nmki represents the error term of gene expression caused by other factors not considered by the model, which obeys an independent and identically distributed normal distribution. n represents the sample array index to which the gene expression data belongs, m represents the dye index to which the gene expression data belongs, and k represents the sample pathway index to which the gene expression data belongs. Finally, a heterogeneous error model is constructed to solve the problem of inaccurate estimation in the case of small samples. The error pooling prior is constructed using a statistical method based on Bayesian hierarchical modeling and gene expression level difference detection. The total error variation is decomposed into two levels: technical and biological. The first and second levels are: y nmki =x nmk +θ nmki x nmk =μ+g n +c m +r nm +b nmk Among them, x nmk represents the true expression level of gene i under the nth array, mth dye and kth sample pathway, μ represents the average expression level of the overall observations, and g n Indicates the influence level of gene on the observed value, c m The differences in gene expression are related to different cell types, r nm Indicates the expression difference caused by the change of specific environmental conditions, b nmk It represents the biological error of gene i under the same array, dye and sample pathway. The first layer is designed to capture the array technical variation caused by the experimental error component. The second layer is designed to capture the biological error with different variability for each different combination of genes and conditions. Through the biological technology of microarray gene significance analysis method and statistical method for detecting differences in gene expression levels, combined with genome association analysis and statistical modeling, the algorithm comprehensively mines the correlation and difference of gene expression in microarray data, providing a more comprehensive analysis method for in-depth understanding of gene function and the overall gene information of biological processes.

2. The animal multiple birth gene rapid diagnosis system based on biotechnology according to claim 1, characterized in that: The sample collection module obtains biological data from biological samples accurately and losslessly, ensuring that the collected biological data remains in its original state, thereby providing reliable and accurate genetic data for subsequent genetic analysis.

3. The animal multiple birth gene rapid diagnosis system based on biotechnology according to claim 1, characterized in that: The gene extraction module ensures that high-quality gene data is obtained from the collected samples through an efficient gene extraction method, providing a reliable basis for subsequent sequencing and analysis to ensure the accuracy of subsequent gene sequencing.

4. The animal multiple birth gene rapid diagnosis system based on biotechnology according to claim 1, characterized in that: The gene sequencing module ensures that detailed information on animal multiple birth genes, including genetic variation factors, is obtained by accurately sequencing gene data, thereby providing accurate and comprehensive gene data for subsequent data analysis.

5. The animal multiple birth gene rapid diagnosis system based on biotechnology according to claim 1, characterized in that: The gene information model construction module includes a gene information analysis unit, which performs in-depth analysis on the variation of multiple birth genes based on the constructed personalized gene information model to identify genetic variations related to the multiple birth state and provide the system with more accurate and diverse multiple birth gene information.

6. The animal multiple birth gene rapid diagnosis system based on biotechnology according to claim 1, characterized in that: The rapid diagnosis module proposes a rapid diagnosis algorithm for multiple births based on variance modeling analysis, which ensures that the system can provide rapid and accurate diagnosis results in a short time by quickly screening out genetic information related to animal multiple births from large-scale genetic data.

7. The animal multiple birth gene rapid diagnosis system based on biotechnology according to claim 6, characterized in that: The specific algorithm for rapid diagnosis of multiple birth genes based on variance modeling analysis is as follows: First, a subset of important gene data is selected, and the association between multiple birth genes and phenotypes is found in the selected subset. Assume that I n represents the genotype of the nth multiple-birth gene, n represents the index of the number of genotypes of the multiple-birth gene, J = {j1, j2, ..., j m } represents the observed phenotypic data, j m represents the phenotypic value of the mth animal individual, where m is the index of the number of phenotypic data. For any multiple birth gene I n , the F statistic corresponding to a single multiple birth gene between multiple birth genes and phenotypic data J is calculated by the variance analysis test statistic of multiple birth genes: Among them, F(I n ,J) indicates a single multiple birth gene I n The ANOVA F statistic, E B (I n ,J) indicates multiple birth gene I n The between-group sum of squares in the total sum of squares for phenotype J, E T (I n ,J) indicates multiple birth gene I n The total sum of squares for phenotype J, P represents the number of animals, Expressed as the degrees of freedom between groups, for each pair of multiple birth genes (I n ,I l ), where l represents the index of the number of genotypes of the multiple-birth gene. The F statistic corresponding to the multiple-birth gene pair between the multiple-birth gene pair and the phenotype data J is calculated to measure their association with the phenotype J. The formula is: Among them, F(I n ,I l ,J) indicates two polygynous genes I n and I l The ANOVA F statistic, E B (I n ,I l ,J) indicates two polygynous genes I n and I l The sum of squares between groups is used to measure the proportion of inter-group variation in the total variation between multiple birth genes and phenotype J, E T (I n ,I l ,J) indicates two polygynous genes I n and I l The total sum of squares between λ and λ represents the number of genotype categories of the multiple birth gene; secondly, by introducing the upper limit value U of the error term H with U K For E B (I n ,I l ,J) is updated and restricted, and the update formula is: HAVE BEEN B (I n ,I l ,J)=E B (I n ,J)+U H +U K Among them, for the difference term U between group H and other groups in the variance analysis test H The calculation formula is: where x H represents the number of individuals in the genome H, and h is represented by n When the value is 1, l A subgroup formed by the genotype combination, x h represents the number of individuals in the gene subgroup h, T H It is expressed as the sum of the phenotypic values ​​of individuals in group H, l h Indicates T H The lower bound of is: u h Indicates T H The upper bound of is: represents the phenotypic value of the i-th individual in group H, i represents the phenotypic data index of the individual in group H, and for the difference term U between group K and other groups in the variance analysis test K The calculation formula is: where x K represents the number of individuals in genome K, k represents the number of individuals in I n When the value is 1, l Another subgroup formed by the genotype combination, T K It is expressed as the sum of the phenotypic values ​​of individuals in group K, l k Indicates T K The lower bound of is: u k Indicates T K The upper bound of is: represents the phenotypic value of the i-th individual in group K, i represents the phenotypic data index of the individual in group K, for each pair of multiple birth genes (I n ,I l ), calculate the F statistic of the multiple birth gene, and use the upper limit value for truncation, then perform a permutation-based test to obtain the maximum multiple birth gene F statistic for each permutation; finally, to resolve the complex nonlinear relationship between genes, a genomic nonlinear relationship model was constructed to more comprehensively capture the association between multiple birth genes. For each pair of multiple birth genes (I n ,I l ), through the kernel function Kernel(I n ,I l ) measures the nonlinear relationship between multiple gene pairs, and combines the kernel function Kernel(I n ,I l ) and the F statistic of the multiple birth gene to obtain the statistical significance of the gene association: Where N represents the total number of genotypes of the multiple birth gene, J random represents a random permutation of J. The reference distribution is generated through multiple permutations. According to the permutation test results, the significance threshold α is determined. The gene pairs were considered significant and had potential association with polygynous genes.

8. The animal multiple birth gene rapid diagnosis system based on biotechnology according to claim 1, characterized in that: The technology update and maintenance module ensures that the system can cope with the evolving biotechnology field by regularly updating the system's genetic analysis algorithms and biotechnology methods, while performing maintenance to maintain the system's stability and reliability.

9. The animal multiple birth gene rapid diagnosis system based on biotechnology according to claim 1, characterized in that: The result presentation and reporting module ensures that the diagnostic results are presented in a friendly form by utilizing intuitive chart information and provides detailed reports, so that users can intuitively understand the diagnostic conclusions of the system in order to better apply the results.

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