Gene detection management system and method

Abstract

The invention discloses a gene detection management system and method. The method comprises the following steps: S1, generating a standardized high-dimensional gene detection data matrix for training; s2, performing global feature analysis on the high-dimensional gene detection data matrix; s3, designing a forward diffusion process and a reverse generation process of the Diffusion model based on a result of the global feature analysis; s4, carrying out global pre-training on the Diffusion model; s5, performing regional layering processing on the Diffusion model based on the selected specific region; s6, carrying out local fine tuning training on the layered Diffusion model; s7, introducing bioinformatics priori knowledge as a constraint condition to regulate and control a reverse generation process of the Diffusion model; and S8, generating synthetic gene detection data by using the Diffusion model subjected to local fine tuning training. The gene mutation mode matching degree is improved by 13.5%, and the expression consistency of key gene loci is improved by 9.8%.

Description

Technical Field

[0001] The present invention relates to the technical field of gene detection management, and particularly to a gene detection management system and method. Background Art

[0002] With the development of bioinformatics and artificial intelligence technologies, gene detection has been widely applied in the fields of medical diagnosis, precision medicine, and drug research and development. Gene detection data contains rich genetic information, and its analysis results can be used for key applications such as disease risk assessment, genetic disease screening, and personalized treatment.

[0003] Currently, the analysis of gene detection data mainly relies on the collection and modeling of real genomic data. However, the acquisition of real gene detection data has the following limitations: First, gene data has a high degree of privacy, and directly sharing and using real data may lead to the risk of privacy leakage. Second, gene detection data presents high-dimensional, sparse, and complex characteristics, and traditional statistical modeling methods are difficult to simultaneously consider the global distribution characteristics and the expression patterns of local key gene loci, resulting in limited quality and applicability of data generation. In addition, due to the fact that disease research and drug screening application scenarios usually require large-scale gene data, however, limited by the collection cost and ethical restrictions of real data, existing methods are difficult to obtain sufficient high-quality samples, which affects the in-depth development of gene detection research.

[0004] In recent years, data generation methods based on deep learning, such as generative adversarial networks and variational autoencoders, have begun to be applied to the synthesis of gene detection data. However, existing methods still have deficiencies in practical applications: First, the GAN training process is prone to mode collapse, and the diversity of generated data is limited, making it difficult to comprehensively cover the complex variation characteristics of real gene data. Second, the data generated by VAE is prone to the problem of over-smoothing and is difficult to accurately capture the high-order dependence structure of gene data.

[0005] In summary, existing gene detection data generation methods still have obvious deficiencies in privacy protection, high-fidelity data generation, and complex structure learning, and are difficult to meet the needs of medical research, precision medicine, and multi-institutional data sharing. Therefore, there is an urgent need for a new method that can improve the quality of gene detection data generation while ensuring data privacy and security, making the synthetic data closer to real data in statistical characteristics, mutation patterns, and biological characteristics, and being applicable to medical research, drug development, and cross-institutional data sharing scenarios. Summary of the Invention

[0006] An object of the present invention is to propose a gene detection management system and method. The matching degree of gene mutation patterns of the present invention has been increased by 13.5%, and the expression consistency of key gene loci has been increased by 9.8%.

[0007] A gene detection management method according to an embodiment of the present invention includes the following steps:

[0008] S1. Perform format conversion, noise filtering, and feature extraction of specific gene loci on the original gene detection data to generate a standardized high-dimensional gene detection data matrix for training;

[0009] S2. Perform global feature analysis on the high-dimensional gene detection data matrix to determine the global distribution characteristics and statistical laws of the high-dimensional gene detection data matrix;

[0010] S3. Based on the results of global feature analysis, the forward diffusion process and reverse generation process of the Diffusion model are designed. The forward diffusion process generates intermediate distribution data by gradually adding noise, and the reverse generation process realizes the synthesis of gene detection data by gradually removing noise.

[0011] S4. Perform global pre-training on the Diffusion model, using the standardized high-dimensional gene detection data matrix as the training data input, and enable the Diffusion model to learn the overall distribution characteristics of the gene detection data through the global pre-training stage;

[0012] S5. According to the results of the global pre-training phase, high mutation frequency regions, key gene loci and gene fragments of specific research directions are selected, and the Diffusion model is subjected to regional stratification processing based on the selected specific regions;

[0013] S6. Perform local fine-tuning training on the layered Diffusion model, and optimize the generation capability of the layered Diffusion model using regional distribution characteristics;

[0014] S7. In the local fine-tuning training stage, bioinformatics prior knowledge is introduced as a constraint to regulate the reverse generation process of the Diffusion model;

[0015] S8. Use the Diffusion model that has been locally fine-tuned to generate synthetic gene detection data, where the synthetic gene detection data is consistent with the real gene detection data in terms of global distribution characteristics and local characteristics within a specific region, and apply the synthetic gene detection data to medical research, drug development, multi-center data sharing and collaborative analysis.

[0016] Optionally, the S1 includes the following steps:

[0017] S11. Convert the format of the original genetic testing data, standardize the genetic testing data generated by different sequencing platforms, map the base sequences using unified coding rules, convert non-standard format data into a compatible format, and form a high-dimensional genetic testing data matrix Among them, m represents the number of samples, n represents the total number of gene loci, and each element X i,j represents the measured value of the i-th sample at the j-th gene locus;

[0018] S12. Perform noise filtering on the high-dimensional gene detection data matrix, remove low-quality loci introduced by sequencing errors, eliminate gene fragments with insufficient sequencing depth, and correct systematic biases caused by differences in sequencing platforms;

[0019] S13. Extract features of specific gene loci from the high-dimensional gene detection data matrix after noise filtering. Based on the gene mutation rate, gene expression correlation, and biological function annotation, screen out a set G of key gene loci with research value k :

[0020]

[0021] Among them, represents the mutation rate of the j-th gene locus in all samples, and T r is the mutation rate screening threshold, represents the annotation result of this gene locus in the biological function database. When the mutation rate of the gene locus is greater than T r and it has a biological function annotation, it is included in the set G of key gene loci k ;

[0022] S14. Reconstruct the standardized high-dimensional gene detection data matrix X * for training in combination with the set of key gene loci, and perform normalization processing.

[0023] Optionally, the S2 includes the following steps:

[0024] S21. Calculate the global gene locus distribution parameters of the high-dimensional gene detection data matrix. The global gene locus distribution parameters include the gene locus mean μ j , standard deviation σ j , coefficient of variation CV j , and distribution skewness S j ;

[0025] S22. Calculate the mutation frequency of the high-dimensional gene detection data matrix, and statistically calculate the mutation incidence rate of each gene locus based on the binary mutation marker matrix M:

[0026]

[0027] Among them, F j represents the mutation frequency of the j-th gene locus, M i,j takes a value of 1 indicating that the i-th sample has a mutation at the j-th gene locus, and 0 indicating no mutation. The mutation frequency Fj Reflect the mutation ratio of this locus in the overall sample;

[0028] S23. Calculate the gene expression features of the high-dimensional gene detection data matrix, calculate the gene expression correlation between different samples based on the gene expression profile data, and define the sample correlation matrix R:

[0029]

[0030] where R i,k represents the correlation between sample i and sample k at the gene expression level, n is the total number of gene loci, and are the standardized expression values of sample i and sample k at gene locus j, respectively;

[0031] S24. Construct the global feature vector T j =(μ j ,σ j ,CV j ,S j ,F j ), and the global feature vector is used in the global pre-training stage of the Diffusion model.

[0032] Optionally, the S3 includes the following steps:

[0033] S31. Design the forward diffusion process of the Diffusion model based on the results of global feature analysis, define the time step t, assume that the standardized representation of the original gene detection data is x0, and use the predetermined noise scheduling parameter β t Calculate α t =1-β t and its cumulative product The forward diffusion process gradually adds noise to generate intermediate distribution data according to the following formula:

[0034]

[0035] where x t is the data representation at time step t, ∈ t is the noise drawn from the Gaussian distribution;

[0036] S32. Design the reverse generation process of the Diffusion model based on the intermediate distribution data generated by the forward diffusion process, define the denoising function ∈ θ (x t ,t) and the corresponding noise standard deviation parameter σ t , and the reverse generation process gradually denoises to realize the synthesis of gene detection data:

[0037]

[0038] Among them, x t-1 is the data representation at time step t - 1, and z t is the noise sampled from the standard normal distribution until t = 0 to obtain the synthetic gene detection data x0.

[0039] Optionally, the S5 includes the following steps:

[0040] S51. Based on the results of the global pre-training stage, calculate the mutation frequency F j and the gene expression feature correlation R i,k ;

[0041] S52. Select key gene loci, and form a set of key gene loci G K :

[0042]

[0043] Among them, T R is the gene expression correlation screening threshold, represents the maximum expression correlation of gene locus j in all samples;

[0044] S53. Select gene fragments concerned with specific research directions, define a set of target genes G T , and take the intersection with the set of key gene loci G K to form the final selected region set G S :

[0045] G S = G K ∩G T ;

[0046] Among them, G T is provided by domain experts or biological databases;

[0047] S54. Based on the final selected region set G S perform regional stratification processing on the Diffusion model, cluster the gene loci within the final selected region set according to the mutation frequency and expression pattern, and generate a stratified group C:

[0048] C k = {j∣d(G S ,j)<T C};

[0049] Among them, d(G S, j) is the distance metric from gene locus j to other genes within the finally selected region set, and T C is the grouping threshold to ensure that gene loci within the same group are similar in mutation frequency and expression pattern;

[0050] S56. Calculate the regional distribution characteristics of each stratified group T k , including the mean mutation frequency within the group standard deviation and the mean expression correlation

[0051] Optionally, S7 includes the following steps:

[0052] S71. Use the regional distribution characteristics T k as the initial condition for local fine-tuning training of the Diffusion model, and calculate the local feature vectors based on each stratified group T k ′ ;

[0053] S72. Adjust the denoising function ∈ θ (x t , t, C k ) in the reverse generation process based on the Diffusion model after regional stratification, and perform denoising regulation in combination with the regional distribution characteristics of the stratified group;

[0054] S73. Introduce bioinformatics prior knowledge in the local fine-tuning training stage, and construct a biological prior constraint function Ψ(x t , C k ) for regulating the reverse generation process of the Diffusion model. The biological prior constraints include:

[0055]

[0056] Among them, is the indicator function, F j is the mutation frequency, R i,k is the gene expression correlation, and λ1 and λ2 are adjustment weight parameters to make the generated data conform to the key locus characteristics of a specific disease phenotype;

[0057] S74. Use the target loss function L gen to perform local fine-tuning training and optimize the generation ability of the Diffusion model in different stratified regions. The loss function is defined as follows:

[0058]

[0059] Among them, the first term is the noise prediction error of the standard Diffusion model, and the second term is the bioinformatics prior constraint to make the generated data conform to biological logic.

[0060] Optionally, S8 includes the following steps:

[0061] S81. Use a Diffusion model that has been locally fine-tuned. Initialize the synthetic gene detection data matrix based on the target sample quantity and place it in a standardized random distribution state;

[0062] S82. Gradually remove noise according to the reverse generation process of the Diffusion model, making the synthetic gene detection data matrix gradually approach the distribution of the target gene detection data from the initial random state, and ensuring that each generation step can retain the information of key gene loci and conform to the global data characteristics;

[0063] S83. Calculate the distribution similarity between the generated synthetic gene detection data and the real gene detection data by combining the global distribution characteristics and regional distribution features, and compare the statistical characteristics of key gene loci, so that the generated data is consistent with the real data in terms of mutation patterns, gene expression levels, and correlation structures;

[0064] S84. Use an optimization strategy to evaluate and adjust the quality of the generated data, and optimize the overall distribution and specific regional features of the generated data through various data matching methods;

[0065] S85. Apply the finally generated synthetic gene detection data to medical research, drug development, and multi-center data sharing, and provide privacy protection and data security guarantees in a multi-institutional cooperation environment.

[0066] A gene detection management system for a gene detection management method, including the following modules:

[0067] A data preprocessing module, used to perform format conversion, noise filtering, and feature extraction of specific gene loci on the original gene detection data to form a standardized high-dimensional gene detection data matrix;

[0068] A global feature analysis module, used to perform global feature analysis on the standardized high-dimensional gene detection data matrix, calculate the statistical characteristics, mutation frequencies, and gene expression levels of gene loci, and construct a global feature vector;

[0069] A Diffusion model construction module, used to construct the forward diffusion process and reverse generation process of the Diffusion model based on the results of global feature analysis, gradually add noise through the forward diffusion process to generate intermediate distribution data, and denoise through the reverse generation process to restore the key features of the gene detection data;

[0070] A pre-training module, used to perform global pre-training on the Diffusion model;

[0071] The regional stratification processing module is used to select regions with high mutation frequencies, key gene loci, and gene fragments of specific research directions based on the global pre-training results, and perform regional stratification processing on the Diffusion model based on the selected regions;

[0072] The local fine-tuning training module is used to perform local fine-tuning training on the stratified Diffusion model, optimize the model's generation ability in combination with regional distribution characteristics, and introduce prior knowledge of bioinformatics during the training process, including gene expression levels, mutation rates, and key loci related to specific disease phenotypes, as constraints for the reverse generation process;

[0073] The data generation module is used to apply the Diffusion model that has undergone local fine-tuning training to generate synthetic gene detection data;

[0074] The data application module is used to apply the finally generated synthetic gene detection data to medical research, drug development, and multi-center data sharing, and provide privacy protection and data security policies.

[0075] The beneficial effects of the present invention are:

[0076] (1) By calculating the mutation frequency and expression correlation of gene loci in the global pre-training stage, the present invention screens out key gene loci and divides them into different regional levels, enabling the Diffusion model to perform local optimization for the data characteristics of different regions. Through stratification processing and regional fine-tuning, the model can achieve more accurate data generation in key gene regions, effectively reducing the KL divergence between synthetic data and real data, improving the fitting degree of the mutation site distribution. Compared with the traditional Diffusion generation method without regional optimization, the mutation pattern matching degree has increased by 13.5%, and the expression consistency of key gene loci has increased by 9.8%.

[0077] (2) The present invention introduces prior knowledge constraints of bioinformatics in the reverse generation process of the Diffusion model, including gene expression levels, mutation rates, and key loci related to specific disease phenotypes, enabling the generated synthetic gene detection data to conform to biological laws. The prior constraint function is used to regulate the denoising process to ensure the biological consistency of the data during the generation process. When generating tumor-related gene data, the present invention can dynamically adjust the mutation frequency to make it conform to the mutation pattern of specific cancer subtypes, while maintaining a reasonable co-expression relationship in the gene expression correlation matrix.

[0078] (3) In the process of generating data, the present invention combines a privacy protection mechanism, adopts differential privacy and data de-identification strategies to ensure that the generated data can meet the needs of medical research without disclosing individual identity information. Synthetic data with real statistical characteristics but without the original individual identity information is generated through the Diffusion model, enabling it to be shared and analyzed under the premise of data privacy protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0080] Figure 1 is a flowchart of a gene detection management system and method proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0081] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are all simplified schematic diagrams, only illustrating the basic structure of the present invention in a schematic manner, so they only show the components related to the present invention.

[0082] Refer to Figure 1 , a gene detection management method, comprising the following steps:

[0083] S1. Perform format conversion, noise filtering, and feature extraction of specific gene loci on the original gene detection data to generate a standardized high-dimensional gene detection data matrix for training;

[0084] S2. Perform global feature analysis on the high-dimensional gene detection data matrix to determine the global distribution characteristics and statistical laws of the high-dimensional gene detection data matrix;

[0085] S3. Based on the results of the global feature analysis, design the forward diffusion process and the reverse generation process of the Diffusion model. The forward diffusion process generates intermediate distribution data by gradually adding noise, and the reverse generation process realizes the synthesis of gene detection data by gradually denoising;

[0086] S4. Perform global pre-training on the Diffusion model, use the standardized high-dimensional gene detection data matrix as the training data input, and enable the Diffusion model to learn the overall distribution characteristics of the gene detection data through the global pre-training stage;

[0087] S5. Select high mutation frequency regions, key gene loci, and gene fragments of specific research directions based on the results of the global pre-training stage, and perform regional stratification processing on the Diffusion model based on the selected specific regions;

[0088] S6. Perform local fine-tuning training on the layered Diffusion model, and optimize the generation capability of the layered Diffusion model using regional distribution characteristics;

[0089] S7. In the local fine-tuning training stage, bioinformatics prior knowledge is introduced as a constraint to regulate the reverse generation process of the Diffusion model;

[0090] S8. Use the Diffusion model that has been trained through local fine-tuning to generate synthetic genetic testing data. The synthetic genetic testing data is consistent with the real genetic testing data in terms of global distribution characteristics and local characteristics within a specific area. The synthetic genetic testing data is applied to medical research, drug development, multi-center data sharing and collaborative analysis.

[0091] In this implementation, S1 includes the following steps:

[0092] S11. Convert the format of the original genetic testing data, standardize the genetic testing data generated by different sequencing platforms, map the base sequences using unified coding rules, convert non-standard format data into a compatible format, and form a high-dimensional genetic testing data matrix Among them, m represents the number of samples, n represents the total number of gene loci, and each element X i,j Represents the measured value of the i-th sample at the j-th gene locus;

[0093] S12. Noise filtering is performed on the high-dimensional gene detection data matrix to remove low-quality sites introduced by sequencing errors, eliminate gene fragments with insufficient sequencing depth, and correct systematic deviations caused by differences in sequencing platforms;

[0094] S13. Extract the features of specific gene loci from the high-dimensional gene detection data matrix after noise filtration, and screen out the key gene loci set G with research value based on gene mutation rate, gene expression correlation and biological function annotation. k :

[0095]

[0096] in, represents the mutation rate of the jth gene locus in all samples, T r Filter threshold for mutation rate, Represents the annotation result of the gene locus in the biological function database. When the mutation rate of the gene locus is greater than T r If it has biological function annotation, it will be included in the key gene locus set G k ;

[0097] S14. Reconstruct the standardized high-dimensional gene detection data matrix X for training by combining the set of key gene loci * , and perform normalization processing.

[0098] In this embodiment, S2 includes the following steps:

[0099] S21. Calculate the global gene locus distribution parameters of the high-dimensional gene detection data matrix. The global gene locus distribution parameters include the gene locus mean μ j , standard deviation σ j , coefficient of variation CV j , and distribution skewness S j ;

[0100] S22. Calculate the mutation frequency of the high-dimensional gene detection data matrix. Based on the binary mutation marker matrix M, statistically calculate the mutation incidence rate of each gene locus:

[0101]

[0102] Among them, F j represents the mutation frequency of the j-th gene locus. M i,j takes a value of 1 indicating that the i-th sample has a mutation at the j-th gene locus, and 0 indicating no mutation. The mutation frequency F j reflects the mutation proportion of this locus in the overall samples;

[0103] S23. Calculate the gene expression characteristics of the high-dimensional gene detection data matrix. Based on the gene expression profile data, calculate the gene expression correlation between different samples, and define the sample correlation matrix R:

[0104]

[0105] Among them, R i,k represents the correlation between sample i and sample k at the gene expression level. n is the total number of gene loci, and are the standardized expression values of sample i and sample k at gene locus j respectively;

[0106] S24. Construct the global feature vector T j =(μ j ,σ j ,CV j ,S j ,F j ), and the global feature vector is used for the global pre-training stage of the Diffusion model.

[0107] In this embodiment, S3 includes the following steps:

[0108] S31. Design the forward diffusion process of the Diffusion model based on the results of global feature analysis. Define the time step t. Let the standardized representation of the original gene detection data be x0, and adopt the predetermined noise scheduling parameter β t Calculate α t = 1 - β t and its cumulative product The forward diffusion process gradually adds noise to generate intermediate distribution data according to the following formula:

[0109]

[0110] where, x t is the data representation at time step t, ∈ t is the noise drawn from the Gaussian distribution;

[0111] S32. Design the reverse generation process of the Diffusion model based on the intermediate distribution data generated by the forward diffusion process. Define the denoising function ∈ θ (x t , t) and the corresponding noise standard deviation parameter σ t , and the reverse generation process gradually denoises to realize the synthesis of gene detection data:

[0112]

[0113] where, x t-1 is the data representation at time step t - 1, z t is the noise drawn from the standard normal distribution, until t = 0 to obtain the synthetic gene detection data x0.

[0114] In this embodiment, S5 includes the following steps:

[0115] S51. Based on the results of the global pre-training stage, calculate the mutation frequency F j of each gene locus in the gene detection data and the gene expression feature correlation R i,k ;

[0116] S52. Select key gene loci, and form a set G of key gene loci based on gene function database annotation combined with mutation frequency and gene expression feature correlation K :

[0117]

[0118] where, T R is the gene expression correlation screening threshold, represents the maximum expression correlation of gene locus j in all samples;

[0119] S53. Select the gene fragments concerned with specific research directions, and define the target gene set G based on biological knowledge and research objectives T , and take the intersection with the key gene locus set G K to form the final selected region set G S :

[0120] G S = G K ∩G T ;

[0121] Among them, G T is provided by domain experts or biological databases;

[0122] S54. Based on the final selected region set G S , perform regional stratification processing on the Diffusion model, cluster the gene loci within the final selected region set according to mutation frequency and expression pattern, and generate the stratification group C:

[0123] C k = {j∣d(G S ,j) < T C};

[0124] Among them, d(G S ,j) is the distance metric from gene locus j to other genes within the final selected region set, and T C is the grouping threshold to ensure that the gene loci within the same group are similar in mutation frequency and expression pattern;

[0125] S56. Calculate the regional distribution characteristics T k of each stratification group, including the mean value of the mutation frequency within the group standard deviation and the mean value of the expression correlation

[0126] In this embodiment, S7 includes the following steps:

[0127] S71. Use the regional distribution characteristics T k as the initial condition for local fine-tuning training of the Diffusion model, and calculate the local feature vector T k ′ based on each stratification group;

[0128] S72. Adjust the denoising function ∈ θ (x t ,t,C k ) in the reverse generation process based on the Diffusion model after regional stratification, and perform denoising regulation in combination with the regional distribution characteristics of the stratification group;

[0129] S73. Introduce bioinformatics prior knowledge in the local fine-tuning training stage, and construct a biological prior constraint function Ψ(x t ,C k ) for regulating the reverse generation process of the Diffusion model. The biological prior constraints include:

[0130]

[0131] Among them, is the indicator function, F j is the mutation frequency, R i,k is the gene expression correlation, and λ1 and λ2 are adjustment weight parameters to make the generated data conform to the key locus characteristics of a specific disease phenotype;

[0132] S74. Use the target loss function L gen to perform local fine-tuning training to optimize the generation ability of the Diffusion model in different hierarchical regions. The loss function is defined as follows:

[0133]

[0134] Among them, the first term is the noise prediction error of the standard Diffusion model, and the second term is the bioinformatics prior constraint to make the generated data conform to biological logic.

[0135] In this embodiment, S8 includes the following steps:

[0136] S81. Use the Diffusion model after local fine-tuning training to initialize the synthetic gene detection data matrix based on the target sample quantity and place it in a standardized random distribution state;

[0137] S82. Gradually remove noise according to the reverse generation process of the Diffusion model, so that the synthetic gene detection data matrix gradually approaches the distribution of the target gene detection data from the initial random state, and each generation step can retain the information of key gene loci and conform to the global data characteristics;

[0138] S83. Combine the global distribution characteristics and regional distribution features to calculate the distribution similarity between the generated synthetic gene detection data and the real gene detection data, and compare the statistical characteristics of the key gene loci to make the generated data consistent with the real data in terms of mutation patterns, gene expression levels, and correlation structures;

[0139] S84. Use an optimization strategy to evaluate and adjust the quality of the generated data, and optimize the overall distribution and specific regional characteristics of the generated data through various data matching methods;

[0140] S85. Apply the finally generated synthetic gene detection data to medical research, drug development, and multi-center data sharing, and provide privacy protection and data security guarantees in a multi-institutional cooperation environment.

[0141] A gene detection management system for a gene detection management method, including the following modules:

[0142] A data preprocessing module for performing format conversion, noise filtering, and feature extraction of specific gene loci on the original gene detection data to form a standardized high-dimensional gene detection data matrix;

[0143] A global feature analysis module for performing global feature analysis on the standardized high-dimensional gene detection data matrix, calculating statistical features, mutation frequencies, and gene expression levels of gene loci, and constructing a global feature vector;

[0144] A Diffusion model construction module for constructing the forward diffusion process and the reverse generation process of the Diffusion model based on the results of global feature analysis, gradually adding noise through the forward diffusion process to generate intermediate distribution data, and denoising and restoring the key features of the gene detection data through the reverse generation process;

[0145] A pre-training module for performing global pre-training on the Diffusion model;

[0146] A regional hierarchical processing module for selecting high-mutation-frequency regions, key gene loci, and gene fragments of interest in specific research directions according to the results of global pre-training, and performing regional hierarchical processing on the Diffusion model based on the selected regions;

[0147] A local fine-tuning training module for performing local fine-tuning training on the hierarchical Diffusion model, optimizing the generation ability of the model in combination with regional distribution features, and introducing prior knowledge of bioinformatics, including gene expression levels, mutation rates, and key loci related to specific disease phenotypes, as constraint conditions for the reverse generation process during training;

[0148] A data generation module for applying the locally fine-tuned Diffusion model to generate synthetic gene detection data;

[0149] A data application module for applying the finally generated synthetic gene detection data to medical research, drug development, and multi-center data sharing, and providing privacy protection and data security policies.

[0150] Example 1:

[0151] During the period from June 2024 to December 2024, a well-known domestic oncology research center (hereinafter referred to as the "research center") faced the problem of gene data sharing and privacy protection in the research of lung cancer targeted therapy. The research center joined forces with three hospitals to jointly participate in the research on the EGFR gene mutation pattern of non-small cell lung cancer patients. However, since the patient's gene test data belongs to sensitive privacy information, the hospitals cannot directly share the real gene sequencing data of the patients, resulting in difficulties in promoting cross-institutional research.

[0152] Under this background, the research center decided to adopt the method of the present invention, a high-fidelity synthetic gene test data generation technology based on the Diffusion model, to generate high-quality synthetic gene data in a safe and compliant manner for the research team to conduct targeted drug screening, gene mutation pattern analysis, and cross-institutional collaborative research.

[0153] On June 10, 2024, the research center obtained the gene test data of 4500 NSCLC patients from the cooperative hospitals. The data format is a standardized VCF file, and each file records the whole-genome sequencing information of the patients, including mutation sites, gene expression levels, and structural variation content.

[0154] After obtaining the data, the research team first pre-processed the data, including:

[0155] Format conversion: Convert the VCF data into a standardized high-dimensional gene test data matrix to ensure that the data formats of different hospitals are consistent.

[0156] Noise filtering: Remove low-quality data with a sequencing depth lower than 30x and eliminate false-positive mutations caused by sequencing errors.

[0157] Key gene screening: Based on the mutation frequency analysis, select the key genes of EGFR, KRAS, TP53, and ALK with high mutation frequencies related to lung cancer.

[0158] After data cleaning and standardization processing, the research team obtained a standardized data set containing 4500 patients and 15000 gene loci, and stored it in a securely isolated computing environment to ensure that patient privacy is not leaked.

[0159] On July 5, 2024, the research team began to train the Diffusion model and conduct global pre-training on the data of 4000 patients to enable the model to learn the global distribution characteristics of gene data.

[0160] Training environment: NVIDIA A100 GPU (80GB) × 4

[0161] Training duration: 48 hours

[0162] Training method: Regional stratification strategy, hierarchical modeling for high-mutation regions and low-mutation regions;

[0163] Goal: Ensure that the mutation patterns of the generated data are consistent with the real data while meeting the privacy protection requirements;

[0164] After training, the research team used the remaining 500 patient data for model evaluation and randomly selected 10,000 synthetic patient data for comparative analysis with the real data.

[0165] Quality verification of synthetic data

[0166] On August 10, 2024, the research team verified the quality of the synthetic data using multiple indicators, including:

[0167] Mutation pattern matching degree, calculating the mutation frequencies of EGFR gene mutation hotspots (such as L858R, T790M); Result: The mutation pattern matching degree of the synthetic data and the real data for EGFR, TP53, and KRAS reached 96.5%;

[0168] Gene expression correlation, calculating the expression correlation between the synthetic data and the real data in different genomic regions, Result: The Pearson correlation between the gene expression levels of the synthetic data and the real data reached 0.92;

[0169] Privacy protection ability, using the differential privacy mechanism to conduct a privacy attack test on the synthetic data to verify the degree of data de-identification; Result: The data recognition rate of the synthetic data in the privacy attack test was less than 0.5%, meeting the medical data sharing standard;

[0170] On September 15, 2024, the research team decided to use the synthetic gene data in a real EGFR targeted drug screening experiment.

[0171] Experimental goal: Simulate patient populations with different EGFR mutation patterns through synthetic gene data to verify the potential applicability of new lung cancer targeted drugs.

[0172] Experimental design:

[0173] Real patient data group (n = 5000);

[0174] Synthetic data group (n = 10000);

[0175] Drug: EGFR-TKI (targeted drug);

[0176] Experimental results (November 1, 2024):

[0177] Index Real data set Synthetic data set Difference (%) EGFR mutation matching degree 96.5％ 95.8％ -0.7％ KRAS mutation matching degree 92.3％ 91.5％ -0.8％ Prediction accuracy of drug response rate 88.4％ 87.6％ -0.8％

[0178] Result analysis:

[0179] The synthetic data is highly consistent with the real data in terms of the matching degree of EGFR and KRAS mutations, with a deviation of less than 1%;

[0180] The accuracy of predicting the drug response rate only differs by 0.8% compared with the real data, demonstrating the usability of the synthetic data;

[0181] The research team successfully used the synthetic data for EGFR mutation pattern analysis, providing support for lung cancer targeted therapy;

[0182] On December 10, 2024, the research center provided 10,000 cases of synthetic gene data generated through the data sharing platform of medical research institutions to three hospitals for joint analysis. The research team ensured data compliance through security protocols and privacy protection mechanisms.

[0183] Hospital A: Use synthetic data for lung cancer biomarker screening;

[0184] Hospital B: Use synthetic data for survival prediction modeling of lung cancer patients;

[0185] Hospital C: Analyze the applicability of synthetic data in multi-omics integration research;

[0186] Share experimental results (January 5, 2025)

[0187]

[0188]

[0189] This embodiment successfully verified the effectiveness of the method of the present invention. The Diffusion model performs excellently in generating synthetic gene detection data, with a mutation pattern matching degree of 96.5% with the real data, a gene expression correlation as high as 0.92, and an identifiable rate in the privacy attack test lower than 0.5%. Through the method of the present invention, the research team completed the screening of EGFR-targeted drugs without using real patient data and promoted the joint research of three hospitals through the medical data sharing platform, providing a safe and efficient solution for future precision medicine and data sharing.

[0190] In the global pre-training stage of the present invention, the mutation frequency and expression correlation of gene loci are calculated to screen out key gene loci, which are divided into different regional levels, enabling the Diffusion model to perform local optimization for data characteristics in different regions. Through hierarchical processing and regional fine-tuning, the model can achieve more accurate data generation in key gene regions, effectively reducing the KL divergence between synthetic data and real data, improving the fitting degree of the mutation site distribution. Compared with the traditional Diffusion generation method without regional optimization, the mutation pattern matching degree has increased by 13.5%, and the expression consistency of key gene loci has increased by 9.8%.

[0191] In the reverse generation process of the Diffusion model of the present invention, prior knowledge constraints in bioinformatics are introduced, including gene expression levels, mutation rates, and key loci related to specific disease phenotypes, enabling the generated synthetic gene detection data to conform to biological laws. The prior constraint function is used to regulate the denoising process to ensure the biological consistency of the data during generation. When generating tumor-related gene data, the present invention can dynamically adjust the mutation frequency to conform to the mutation pattern of a specific cancer subtype, while maintaining a reasonable co-expression relationship in the gene expression correlation matrix.

[0192] In the process of generating data, the present invention combines a privacy protection mechanism, adopting differential privacy and data de-identification strategies to ensure that the generated data can meet the needs of medical research without disclosing individual identity information. Synthetic data with real statistical characteristics but without the original individual identity information is generated through the Diffusion model, enabling it to be shared and analyzed under the premise of data privacy protection.

[0193] As mentioned above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered by the protection scope of the present invention.

Claims

1. A gene detection management method, characterized in that, The steps include: S1. Perform format conversion, noise filtering, and feature extraction of specific gene loci on the original gene detection data to generate a standardized high-dimensional gene detection data matrix for training; S2. Perform global feature analysis on the high-dimensional gene detection data matrix to determine the global distribution characteristics and statistical laws of the high-dimensional gene detection data matrix; S3. Based on the results of global feature analysis, the forward diffusion process and reverse generation process of the Diffusion model are designed. The forward diffusion process generates intermediate distribution data by gradually adding noise, and the reverse generation process realizes the synthesis of gene detection data by gradually removing noise. S4. Perform global pre-training on the Diffusion model, using the standardized high-dimensional gene detection data matrix as the training data input, and enable the Diffusion model to learn the overall distribution characteristics of the gene detection data through the global pre-training stage; S5. According to the results of the global pre-training phase, high mutation frequency regions, key gene loci and gene fragments of specific research directions are selected, and the Diffusion model is subjected to regional stratification processing based on the selected specific regions; S6. Perform local fine-tuning training on the layered Diffusion model, and optimize the generation capability of the layered Diffusion model using regional distribution characteristics; S7. In the local fine-tuning training stage, bioinformatics prior knowledge is introduced as a constraint to regulate the reverse generation process of the Diffusion model; S8. Use the Diffusion model that has been locally fine-tuned to generate synthetic gene detection data, where the synthetic gene detection data is consistent with the real gene detection data in terms of global distribution characteristics and local characteristics within a specific region, and apply the synthetic gene detection data to medical research, drug development, multi-center data sharing and collaborative analysis.

2. The genetic testing management method according to claim 1, wherein The S1 comprises the following steps: S11. Convert the format of the original gene detection data, standardize the gene detection data generated by different sequencing platforms, map the base sequences using a unified coding rule, convert the non-standard format data into a compatible format, and form a high-dimensional gene detection data matrix where m represents the number of samples, n represents the total number of gene loci, and each element X i,j represents the measured value of the i-th sample at the j-th gene locus; S12. Noise filtering is performed on the high-dimensional gene detection data matrix to remove low-quality sites introduced by sequencing errors, eliminate gene fragments with insufficient sequencing depth, and correct systematic deviations caused by differences in sequencing platforms; S13. Extract the features of specific gene loci from the high-dimensional gene detection data matrix after noise filtering, and based on the gene mutation rate, gene expression correlation, and biological function annotation, screen out the set G of key gene loci with research value k : Among them, R(X ′ *,j ) represents the mutation rate of the j-th gene locus in all samples, and T r is the mutation rate screening threshold. F(X ′ *,j ) represents the annotation result of this gene locus in the biological function database. When the mutation rate of the gene locus is greater than T r and it has a biological function annotation, it is included in the set G k of key gene loci; S14. Reconstruct the standardized high-dimensional gene detection data matrix X for training by combining the set of key gene loci * , and perform normalization processing.

3. A gene detection management method according to claim 1, characterized in that The S2 comprises the following steps: S21. Calculate the global gene locus distribution parameters of the high-dimensional gene detection data matrix, where the global gene locus distribution parameters include the gene locus mean μ j , standard deviation σ j , coefficient of variation CV j , and distribution skewness S j ; S22. Calculate the mutation frequency of the high-dimensional gene detection data matrix, and count the mutation incidence of each gene locus based on the binary mutation marker matrix M: Among them, F j represents the mutation frequency of the j-th gene locus, M i,j takes the value of 1 indicating that the i-th sample has a mutation at the j-th gene locus, and 0 indicating no mutation. The mutation frequency F j reflects the mutation proportion of this locus in the overall sample; S23. Calculate the gene expression characteristics of the high-dimensional gene detection data matrix, calculate the gene expression correlation between different samples based on the gene expression spectrum data, and define the correlation matrix R between samples: Among them, R i,k represents the correlation between sample i and sample k at the gene expression level, n is the total number of gene loci, and are the normalized expression values of sample i and sample k at gene locus j, respectively; S24. Construct a global feature vector T based on the global gene locus distribution parameters, mutation frequencies, and gene expression characteristics j =(μ j ,σ j ,CV j ,S j ,F j ), and the global feature vector is used in the global pre-training stage of the Diffusion model.

4. A gene detection management method according to claim 1, wherein The S3 comprises the following steps: S31. Design the forward diffusion process of the Diffusion model based on the results of global feature analysis. Define the time step t. Let the standardized representation of the original gene detection data be x0, and adopt the predetermined noise scheduling parameter β t Calculate α t = 1 - β t And its cumulative product The forward diffusion process gradually adds noise to generate intermediate distribution data according to the following formula: where x t is the data representation at time step t, and ∈ t is the noise drawn from a Gaussian distribution; S32. Design the reverse generation process of the Diffusion model based on the intermediate distribution data generated by the forward diffusion process, and define the denoising function ∈ θ (x t , t) and the corresponding noise standard deviation parameter σ t . The reverse generation process gradually denoises to realize the synthesis of gene detection data: where x t-1 is the data representation at time step t - 1, and z t is the noise drawn from the standard normal distribution until t = 0 to obtain the synthetic gene detection data x0.

5. A gene detection management method according to claim 1, characterized in that, The S5 comprises the following steps: S51. Calculate the mutation frequency F of each gene locus in the gene detection data and the correlation R with gene expression characteristics based on the results of the global pre-training phase j and the correlation R with gene expression characteristics i,k ; S52. Select key gene loci, and form a set G of key gene loci based on gene function database annotation combined with the correlation between mutation frequency and gene expression characteristics K : Among them, T R is the screening threshold for gene expression correlation, representing the maximum expression correlation of gene locus j in all samples; S53. Select gene fragments concerned with specific research directions, and define the target gene set G based on biological knowledge and research goals T , and take the intersection with the key gene locus set G K to form the final selected region set G S : G S = G K ∩ G T ; Among them, G T is provided by domain experts or biological databases; S54. Based on the final selected region set G S Perform regional stratification processing on the Diffusion model, cluster the gene loci within the final selected region set according to the mutation frequency and expression pattern, and generate the stratification group C: C k = {j | d(G S , j) < T C}; Among them, d(G S , j) is the distance metric from gene locus j to other genes within the finally selected region set, and T C is the grouping threshold to ensure that gene loci within the same group are similar in mutation frequency and expression pattern; S56. Calculate the regional distribution characteristics T of each stratification group k , including the mean of the within-group mutation frequency Standard deviation and the mean of the expression correlation 6. A gene detection management method according to claim 1, characterized in that The S7 comprises the following steps: S71. Adopt the regional distribution feature T k As the initial condition for local fine-tuning training of the Diffusion model, calculate the local feature vector T based on each hierarchical group k ′ ; S72. Adjust the denoising function in the reverse generation process based on the Diffusion model after regional stratification ∈ θ (x t , t, C k ), and perform denoising regulation by combining the regional distribution characteristics of the stratification group; S73. Introduce bioinformatics prior knowledge in the local fine-tuning training stage to construct a biological prior constraint function Ψ(x t , C k ) for regulating the reverse generation process of the Diffusion model. The biological prior constraints include: Among them, is an indicator function, F j is the mutation frequency, R i,k is the gene expression correlation, and λ1 and λ2 are adjusted weight parameters, which are the key site features for making the generated data conform to the specific disease phenotype; S74. Adopt the target loss function L gen Perform local fine-tuning training to optimize the generation ability of the Diffusion model in different hierarchical regions. The loss function is defined as follows: Among them, the first term is the noise prediction error of the standard Diffusion model, and the second term is the bioinformatics prior constraint, which makes the generated data conform to biological logic.

7. A gene detection management method according to claim 1, characterized in that The S8 comprises the following steps: S81. Use the Diffusion model trained with local fine-tuning to initialize the synthetic gene detection data matrix based on the target sample number and place it in a standardized random distribution state; S82. According to the reverse generation process of the Diffusion model, the noise is gradually removed, so that the synthetic gene detection data matrix gradually approaches the distribution of the target gene detection data from the initial random state, so that each generation step can retain the information of the key gene sites and conform to the global data characteristics; S83. Calculate the distribution similarity between the synthetic gene detection data and the real gene detection data by combining the global distribution characteristics and the regional distribution characteristics, and compare the statistical characteristics of the key gene sites to make the generated data consistent with the real data in terms of mutation pattern, gene expression level and correlation structure; S84. Use optimization strategies to assess and adjust the quality of generated data, and optimize the overall distribution and specific regional characteristics of generated data through multiple data matching methods; S85. The resulting synthetic genetic testing data will be used in medical research, drug development, and multi-center data sharing, while providing privacy protection and data security in a multi-institutional collaborative environment.

8. A gene detection management system for performing the gene detection management method according to any one of claims 1-7, characterized in that, Includes the following modules: The data preprocessing module is used to convert the format of the original gene detection data, filter noise, and extract the features of specific gene loci to form a standardized high-dimensional gene detection data matrix; The global feature analysis module is used to perform global feature analysis on the standardized high-dimensional gene detection data matrix, calculate the statistical characteristics of gene loci, mutation frequency and gene expression level, and construct a global feature vector; Diffusion model construction module, which is used to construct the forward diffusion process and reverse generation process of the Diffusion model based on the results of global feature analysis. It gradually adds noise to generate intermediate distribution data through the forward diffusion process, and removes noise and restores the key features of gene detection data through the reverse generation process. Pre-training module, used for global pre-training of Diffusion model; The regional hierarchical processing module is used to select high mutation frequency regions, key gene sites and gene fragments of specific research directions according to the global pre-training results, and perform regional hierarchical processing on the Diffusion model based on the selected regions; The local fine-tuning training module is used to perform local fine-tuning training on the stratified Diffusion model, optimize the model's generation capability based on regional distribution characteristics, and introduce bioinformatics prior knowledge during the training process, including gene expression levels, mutation rates, and key sites related to specific disease phenotypes, as constraints for the reverse generation process; The data generation module is used to apply the Diffusion model trained with local fine-tuning to generate synthetic gene detection data; The data application module is used to apply the final synthetic gene testing data to medical research, drug development and multi-center data sharing, and provide privacy protection and data security strategies.

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