Tumor mutation prediction system and method

Abstract

The invention relates to the technical field of medical data analysis, in particular to a tumor mutation prediction system and method.The tumor mutation prediction system comprises a data perception intelligent analysis module, an intelligent association regulation engine, a biological mechanism analysis module, a multi-dimensional collaborative prediction module and a verification and clinical conversion module; wherein the data perception intelligent analysis module integrates data of single cell sequencing, spatial omics and the like, identifies features and analyzes and processes the features; an intelligent association regulation and control engine captures data complex association, and performs feature association learning and dynamic weight adjustment; the biological mechanism analysis module simulates a biomolecular behavior process and identifies a potential propagation path and influence of tumor mutation; the multi-dimensional collaborative prediction module fuses the spatial-temporal characteristics and predicts the tumor mutation type in combination with a neural network; and the verification and clinical conversion module analyzes and verifies the prediction result, performs clinical application conversion, and generates treatment suggestions in combination with a database. Therefore, the problems of low mutation recognition accuracy, insufficient data integration and the like in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data analysis, and particularly to a tumor mutation prediction system and method. Background Art

[0002] As a major disease seriously threatening human life and health, the occurrence and development process of tumors are closely related to gene mutations. The advent of new sequencing technologies has made it possible to predict somatic mutations related to cancer. However, due to the great heterogeneity and inconsistency of tumor cells, it is still extremely difficult to accurately identify driver mutations from random mutations. Under this background, new generation prediction methods are striving to break through in the direction of high precision, multi-dimension, and real-time. Accurately predicting tumor mutations is of crucial significance for the early diagnosis, personalized treatment, and prognosis evaluation of tumors.

[0003] However, the current traditional tumor mutation prediction still has certain limitations in terms of accuracy, comprehensiveness, and the ability to capture complex tumor characteristics. It cannot comprehensively consider information in multiple aspects such as the spatial distribution of tumor cells and imaging features. Some methods are not fine enough in feature extraction and model construction, making it difficult to effectively mine complex associations and potential features in the data, resulting in low reliability of prediction results. There are generally situations such as poor accuracy in mutation identification and insufficient data fusion, which cause patients not to obtain diagnostic results in a timely manner and delay the best treatment opportunity. Summary of the Invention

[0004] This application provides a tumor mutation prediction system and method to solve problems such as low accuracy in mutation identification and insufficient data integration in the prior art.

[0005] The first aspect of the embodiments of this application provides a tumor mutation prediction system, including: a data perception and intelligent analysis module, an intelligent association and regulation engine, a biological mechanism analysis module, a multi-dimensional collaborative prediction module, and a verification and clinical transformation module; wherein, the data perception and intelligent analysis module is used to integrate single-cell sequencing, spatial omics, and imaging data, identify data features, and perform analysis and processing; the intelligent association and regulation engine is used to capture complex associations between tumor data, perform feature association learning and dynamically adjust weights; the biological mechanism analysis module is used to simulate the dynamic behavior process of biomolecules, identify potential propagation paths of gene mutations and their effects on biological functions; the multi-dimensional collaborative prediction module is used to fuse the spatial position and time series features of tumor data, combine neural networks, and accurately predict the types of tumor gene mutations; the verification and clinical transformation module is used to analyze and verify the prediction results, perform clinical application transformation of tumor mutations, and generate treatment suggestions in combination with a database.

[0006] Preferably, the data perception and intelligent analysis module includes a cross-source data collector and a feature cube identifier, wherein the cross-source data collector is used to capture single-cell sequencing and spatial omics data in real time, and perform data processing and transmission in combination with the Internet of Things; the feature cube identifier is used to identify key feature information in the data, perform multi-dimensional rotation and combination analysis, and mine hidden features of data such as pathological images and genomes.

[0007] Preferably, the intelligent association regulation engine includes a key feature selector, a feature evolution learning cabin, and a dynamic weight optimization module, wherein the key feature selector is used to screen out key features that are highly correlated with tumor gene mutations and have strong correlations; the feature evolution learning cabin is used to simulate natural selection and genetic algorithms, optimize features, adapt to constantly changing tumor data, and improve feature expression capabilities and discrimination; the dynamic weight optimization module is used to dynamically adjust the weight of data in the model.

[0008] Preferably, the biological mechanism analysis module includes a biomolecular simulator and a mutation meteor tracker, wherein the biomolecular simulator is used to simulate the motion trajectory of molecular interactions, analyze the change rules in a visual manner, and quantify the impact of mutations on tumor phenotypes; the mutation meteor tracker is used to track the propagation path of tumor gene mutations in real time, quickly locate the origin point of the mutation, and at the same time, predict its propagation direction and impact range.

[0009] Preferably, the multi-dimensional collaborative prediction module includes a spatiotemporal longitude and latitude fuser, a mutation probability prediction module and a multi-task learning framework, wherein the spatiotemporal longitude and latitude fuser is used to locate the state of the tumor in different time and space dimensions; the mutation probability prediction module is used to extract key feature information and predict the probability of gene mutation of tumor cells at a specific evolutionary stage.

[0010] Preferably, the verification and clinical transformation module includes a pathway verification module, a clinical database docking interface and a treatment suggestion engine, wherein the pathway verification module is used to verify the relationship between the predicted pathway and the occurrence and development of tumors, and ensure the authenticity and clinical transformability of the mutation prediction mechanism; the clinical database docking interface is used to integrate clinical data from the TCGA and COSMIC databases to generate a mutation-treatment response association map; the treatment suggestion engine is used to automatically generate personalized treatment plans, and make real-time adjustments based on patient feedback.

[0011] The second aspect embodiment of the present application provides a tumor mutation prediction method, including: obtaining target data; performing key feature recognition and analysis based on the target data, capturing complex associations of the data according to the key features, performing feature association learning and dynamically adjusting weights to generate an optimized feature vector, simulating the biomolecular diffusion law of gene mutation based on the optimized feature vector, identifying potential transmission paths and influencing factors, predicting mutation types and clinical phenotypes based on the transmission paths and influencing factors to generate a prediction result; converting the prediction result into clinical treatment suggestions in combination with the clinical data in the database, and dynamically optimizing the personalized treatment plan by integrating the patient treatment feedback through a reinforcement learning algorithm.

[0012] The third aspect embodiment of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement a tumor mutation prediction method as in the above embodiment.

[0013] The fourth aspect embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and the program is executed by a processor to implement a tumor mutation prediction method as in the above embodiment.

[0014] The fifth aspect embodiment of the present application provides a computer program product, including a computer program or instruction to implement a tumor mutation prediction method as in the above embodiment.

[0015] Thus, the present application includes the following beneficial effects: The embodiment of the present application integrates single-cell sequencing, spatial omics, and imaging data through a data perception and intelligent analysis module to achieve multi-dimensional feature analysis and cross-modal data fusion, comprehensively capturing tumor heterogeneity; uses an intelligent association regulation engine to dynamically capture complex data associations and optimize feature weights, enhancing the adaptability of the model to the dynamic changes of tumor data; utilizes a biological mechanism analysis module to simulate molecular behaviors and mutation transmission paths, strengthening the biological interpretability of the prediction results; realizes accurate mutation type prediction through a multi-dimensional collaborative prediction module that fuses spatio-temporal features and neural networks; and finally docks with a clinical database through a verification and clinical transformation module to generate personalized treatment suggestions, forming a full-chain closed loop, and enhancing the accuracy, interpretability, and clinical practical value of tumor mutation prediction. Thus, the problems of low accuracy of mutation identification and insufficient data integration in the prior art are solved.

[0016] The additional aspects and advantages of the present application will be partially given in the following description, partially become apparent from the following description, or be understood through the practice of the present application. Description of the Drawings

[0017] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of embodiments in conjunction with the accompanying drawings, where: Figure 1 It is a schematic structural diagram of a tumor mutation prediction system provided according to an embodiment of the present application; Figure 2 It is a schematic diagram of a data perception intelligent analysis module provided according to an embodiment of the present application; Figure 3 It is a schematic diagram of lung cancer diagnosis and treatment provided according to an embodiment of the present application; Figure 4 It is a schematic diagram of an intelligent association regulation engine provided according to an embodiment of the present application; Figure 5 It is a schematic diagram of a biological mechanism analysis module provided according to an embodiment of the present application; Figure 6 It is a schematic diagram of a multi-dimensional collaborative prediction module provided according to an embodiment of the present application; Figure 7 It is a schematic diagram of a verification and clinical transformation module provided according to an embodiment of the present application; Figure 8 It is a flowchart of a tumor mutation prediction system provided according to an embodiment of the present application; Figure 9 It is a flowchart of a tumor mutation prediction method provided according to an embodiment of the present application; Figure 10 It is a schematic diagram of a tumor mutation prediction method provided according to an embodiment of the present application; Figure 11 It is a schematic structural diagram of an electronic device provided according to an embodiment of the present application. Detailed implementation manners

[0018] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0019] Figure 1 It is a schematic composition diagram of a tumor mutation prediction system provided according to an embodiment of the present application.

[0020] An embodiment of the present application provides a tumor mutation prediction system, and the system 10 includes: A data perception intelligent analysis module 100, an intelligent association regulation engine 200, a biological mechanism analysis module 300, a multi-dimensional collaborative prediction module 400, and a verification and clinical transformation module 500.

[0021] Among them, the data perception and intelligent analysis module 100 is used to integrate single-cell sequencing, spatial omics and imaging data, identify data features, and perform analysis and processing; the intelligent association regulation engine 200 is used to capture the complex associations between tumor data, perform feature association learning and dynamically adjust weights; the biological mechanism analysis module 300 is used to simulate the dynamic behavior process of biological molecules, identify the potential transmission paths of gene mutations and their impact on biological functions; the multi-dimensional collaborative prediction module 400 is used to integrate the spatial position and time series characteristics of tumor data, combined with neural networks, to accurately predict the type of tumor gene mutations; the verification and clinical transformation module 500 is used to analyze and verify the prediction results, conduct clinical application transformation of tumor mutations, and generate treatment recommendations in combination with the database.

[0022] It is understandable that in the embodiment of the present application, the data perception intelligent analysis module integrates single-cell sequencing, spatial omics and imaging data to achieve multi-dimensional feature analysis and cross-modal data fusion, and comprehensively capture tumor heterogeneity; the intelligent association control engine dynamically captures complex data associations and optimizes feature weights to improve the adaptability of the model to dynamic changes in tumor data; the biological mechanism analysis module is used to simulate molecular behavior and mutation propagation paths to enhance the biological interpretability of prediction results; the multi-dimensional collaborative prediction module integrates spatiotemporal features and neural networks to achieve accurate mutation type prediction; and finally, it is verified that the clinical database is connected with the clinical transformation module to generate personalized treatment recommendations, forming a full-chain closed loop, and improving the accuracy, interpretability and clinical practical value of tumor mutation prediction. Thus, the problems of low mutation recognition accuracy and insufficient data integration in the prior art are solved.

[0023] In the embodiment of the present application, the data perception intelligence analysis module 100 also includes: Figure 2 As shown, cross-source data collector and feature cube recognizer.

[0024] Among them, the cross-source data collector is used to capture single-cell sequencing and spatial omics data in real time, and combines with the Internet of Things to process and transmit data; the feature cube identifier is used to identify key feature information in the data, perform multi-dimensional rotation and combination analysis, and mine hidden features of pathological images, genomes and other data.

[0025] It can be understood that in the embodiments of the present application, a cross-source data collector is used to capture single-cell sequencing and spatial omics multi-source biomedical data in real time, and the Internet of Things is used to efficiently process and low-latency transmit the data, ensuring that data from different sources and different types can be accurately synchronized and integrated, providing a real-time and complete data set for subsequent analysis; the Feature Magic Cube Recognizer, through multi-dimensional rotation and combinatorial analysis, deeply mines the key feature information in pathological images and genomic data, discovers hidden pathological features and gene expression patterns, provides a more comprehensive and accurate feature dimension for disease diagnosis, efficacy evaluation, etc., and improves the depth of data analysis and the clinical application value.

[0026] It should be noted that the cross-source data collector is in real-time docking with single-cell sequencing devices, spatial omics instruments and Internet of Things terminals through a standardized protocol interface, and uses an asynchronous non-blocking I / O model to parallelly collect multi-format raw data.

[0027] Asynchronous non-blocking I / O model formula: Where, is the current state; is the event set; is the state transition function; is the infinite loop symbol; is the non-blocking polling event; is to distribute events and execute the callback function; is the event loop.

[0028] For example, in the medical information system of a smart hospital, using an asynchronous non-blocking I / O model can significantly improve the system performance. For example, when multiple medical devices (such as CT, MRI, electrocardiogram monitors, etc.) simultaneously transmit patient detection data to the central database, the asynchronous non-blocking I / O model allows the system to receive and process data requests from other devices without waiting for the data transmission of a certain device to complete, greatly reducing the delay and congestion of data transmission. At the same time, when doctors quickly retrieve multiple types of data such as patient images, test reports, and medical history records in the electronic medical record system, this model can parallelly process multiple data reading tasks, avoid interface lags, enable doctors to formulate treatment plans more smoothly and efficiently, and greatly improve the efficiency and quality of medical services.

[0029] The Feature Rubik's Cube Recognizer automatically identifies differentially expressed genes in single-cell sequencing, tissue microenvironment features in spatial omics, and cell morphological indicators in pathological images through an attention mechanism. Then, it projects the data into an orthogonal feature subspace through tensor decomposition, simulating the "Rubik's Cube rotation" effect to reveal the non-linear associations between different omics. Next, it dynamically generates cross-omics feature combinations through a feature interaction network, quantifies the feature importance through gradient boosting trees, and finally combines graph neural networks to capture cell communication networks and uses variational autoencoders to learn the data manifold structure, mining hidden features to assist clinical decision-making.

[0030] Attention mechanism formula: Among them, is the query vector, the representation of the content to be processed currently; is the key vector, the representation of the content to be matched; is the value vector, the actual content associated with K; is the normalization function; is the size of the last-dimensional feature of K or Q; is the matrix dot product operation; is the attention mechanism.

[0031] Feature interaction network formula: Among them, is the measure or feature of node i at the k-th layer; is the summation over the neighbor nodes j of node i; is the weight matrix at the k-th layer; is the feature vector of node j at the (k-1)-th layer.

[0032] Gradient boosting tree formula: Among them, is the output function of the model, composed of the sum of M components; is the number of terms in the summation; is the function of the m-th sub-module, is its parameter vector.

[0033] Graph neural network formula: Among them, is the hidden state at the l-th layer; is the activation function; is the degree matrix; is the normalized adjacency matrix; is the learnable weight matrix at the l-th layer.

[0034] Variational Autoencoder formula: Among them, is the conditional probability distribution, representing the distribution of the latent variable z given the observation x; is the normal distribution; is the mean function; is the covariance; is another conditional probability distribution, representing the distribution of the observation x given the latent variable z; is a multi-layer perceptron, which takes z as input and outputs the parameters used to calculate of.

[0035] For example, as Figure 3 shown, in the precise diagnosis and treatment of lung cancer, for 100 patients with non-small cell lung cancer, the Feature Magic Cube Recognizer integrates HE-stained pathological images of surgically resected tissues (resolution 0.5μm / pixel), single-cell RNA sequencing data (detecting 10,000 genes per cell on average), and 36-month follow-up clinical prognosis information. It extracts nuclear morphology (127 morphological features such as an average nucleolus diameter of 12μm and a nuclear-cytoplasmic ratio of 0.65), tissue structure (tumor budding degree of grade 3), and interstitial lymphocyte infiltration density (50 lymphocytes per high-power field) from the pathological images, and identifies 23 driver gene mutations such as EGFR (average expression level of 5,247 RPKM), KRAS (mutation frequency of 18%), and ALK fusion (positive rate of 7%), as well as the expression of immune checkpoint genes from the genomic data. By means of multi-dimensional feature correlation analysis, it excavates the hidden pathological-gene associations, helps to accurately identify driver mutations, optimize the diagnosis and treatment plan, and provides key data support for personalized medical decision-making in lung cancer.

[0036] In the embodiment of the present application, the intelligent association regulation engine 200 includes: as Figure 4 shown, a key feature selector, a feature evolution learning module, and a dynamic weight optimization module.

[0037] Among them, the key feature selector is used to screen out key features that are highly correlated with tumor gene mutations and have strong associations; the feature evolution learning module is used to simulate natural selection and genetic algorithms to optimize the features, adapt to the changing tumor data, and improve the feature expression ability and discrimination; the dynamic weight optimization module is used to dynamically adjust the weights of the data in the model.

[0038] It is understandable that in the embodiments of the present application, the key feature selector accurately screens key features highly relevant to tumor gene mutations from multi-omics data, eliminating redundant information to reduce the model complexity; the feature evolution learning module simulates the natural selection mechanism by means of genetic algorithms, dynamically optimizes the feature combination for the continuously mutating tumor data, and continuously enhances the ability of features to distinguish tumor subtypes; the dynamic weight optimization module adjusts the weight distribution of multi-source data such as pathological images and genomic sequencing in the model in real time according to data timeliness and modal relevance, improving the accuracy of tumor mutation prediction.

[0039] It should be noted that key features highly relevant and strongly associated with tumor gene mutations are screened out, and multi-omics data such as genomes, transcriptomes, and pathological images are first processed; from a biological perspective, analyze which features may be related to mutations (such as known driver genes and common abnormal indicators in pathological reports), and by comparing the data differences between the mutation group and the non-mutation group, find out those features that are significantly different in the mutation samples.

[0040] Genetic algorithm formula: Among them, is the initial population; is an individual in the population; is the population size; is the number of iteration rounds; is the maximum number of iteration generations; is the t-th generation population; is the set of individuals after the selection operation; is the fitness function; is the set of new individuals after the crossover operation; is the set of new individuals after the mutation operation; is the next generation population.

[0041] For example, in the scenario of medical disease diagnosis assistance, the feature evolution learning module simulates the operation of genetic algorithms: for lung cancer diagnosis, a feature population consisting of 500 groups of patient data is initially constructed , covering CT image textures (such as gray-level co-occurrence matrix features, including 12 dimensions), blood tumor markers (such as 8 indicators including CEA and SCC), and gene sequencing (such as 10 mutation sites including EGFR), a total of 30-dimensional features. Set the iteration period = 100. In each round t, using the diagnostic accuracy rate (objective function , initially about 65%) as the fitness, select 200 groups of high-quality feature subsets from the current feature set by roulette wheel selection; during the crossover operation, according to the single-point crossover method, recombine the imaging and gene feature segments of different patient feature groups to generate 200 groups of new feature combinations ; for mutation, randomly change the feature values with a probability of 0.01 (such as minor perturbations in tumor marker concentrations, simulation of gene locus mutations), to obtain containing new feature patterns; finally, use to replace 100 groups of inefficient features in , and update it to . After 100 rounds of iteration, the diagnostic accuracy rate of the feature set for lung cancer is improved to 82%, and key combined features such as "lobulation sign in CT image + deletion mutation of exon 19 of EGFR + abnormal elevation of CEA" are mined, which helps to construct a more accurate lung cancer diagnosis model, gradually release the value of medical data features through algorithm iteration, and provide better support for disease diagnosis.

[0042] In the embodiment of the present application, the biological mechanism analysis module 300 includes: as Figure 5 shown, a biomolecular simulator and a mutation meteor tracker.

[0043] Among them, the biomolecular simulator is used to simulate the movement trajectories of molecular interactions, analyze the change rules in a visual way, and quantify the impact of mutations on tumor phenotypes; the mutation meteor tracker is used to track the propagation path of tumor gene mutations in real time, quickly locate the origin point of the mutation, and at the same time, predict its propagation direction and influence range.

[0044] It can be understood that in the embodiment of the present application, the biomolecular simulator simulates the movement trajectories of molecular interactions and visualizes them, accurately quantifying the impact of mutations on tumor proliferation and invasion phenotypes; the mutation meteor tracker tracks the mutation propagation path in real time, quickly locates the origin in bronchial epithelial cells, predicts its propagation direction and influence range of distal metastasis, provides dynamic molecular perspectives and propagation law supports for accurate tumor typing, metastasis early warning and intervention target mining, and helps to block the spread of mutations in advance and optimize the diagnosis and treatment strategies.

[0045] It should be noted that the biomolecular simulator first simulates the movement trajectories of interactions at the atomic level based on the molecular dynamics algorithm; then presents these microscopic movements in a visual form, allowing researchers to intuitively analyze the change rules of molecular interactions over time and environment; at the same time, by calculating binding energy, conformational entropy change, and intermolecular force parameters, the abstract molecular movements are transformed into quantifiable indicators, and then correlated with phenotypes such as tumor cell proliferation, invasion, and drug resistance, accurately quantifying the impact of mutations on tumor phenotypes.

[0046] Molecular dynamics algorithm formula: in, is the mass of the i-th atom; is the position vector; is the resultant force vector; is the second-order differential of the position vector; is the acceleration of the ith particle, which is the second-order derivative of the position vector with respect to time, reflecting the rate of change of position with time.

[0047] The mutation meteor tracker is used to track the propagation path of tumor gene mutations in real time, quickly locate the origin of the mutation, and predict its propagation direction and scope of influence. It first collects sample gene data from different parts and stages of the tumor, and compares and analyzes the differences and correlations between gene mutations; then sorts out the order of appearance and distribution changes of these mutations during tumor development, so as to find the origin of the mutation; at the same time, combined with information such as the tumor growth environment and the patient's physical condition, it predicts the direction in which the mutation may spread next, which new areas of the tumor will be affected, and whether it will even metastasize to other organs. It is like installing a "tracker" for tumor mutations, helping doctors to grasp the "trends" of mutations in advance and better formulate treatment plans.

[0048] For example, in the study of targeted treatment of lung cancer, the biomolecular simulator analyzed cancer cells carrying the EGFR T790M drug-resistant mutation. By simulating the interaction between the EGFR protein and the osimertinib drug molecule, the motion trajectory of the two binding is intuitively presented, and the mutation causes the protein conformation to change, causing the drug binding site to shift. At the same time, it was quantitatively calculated that the binding energy between the drug and the protein after the mutation was reduced by 15%, resulting in a weakened effect of osimertinib in inhibiting the proliferation of cancer cells, intuitively and accurately revealing the drug resistance mechanism, and providing key data support for the development of new targeted drugs or combined treatment plans.

[0049] In the embodiment of the present application, the multi-dimensional collaborative prediction module 400 includes: Figure 6 As shown, the spatiotemporal longitude and latitude fuser and the mutation probability prediction module.

[0050] Among them, the spatiotemporal longitude and latitude fusion device is used to locate the state of the tumor in different time and space dimensions; the mutation probability prediction module is used to extract key feature information and predict the probability of gene mutation of tumor cells at a specific evolutionary stage.

[0051] It is understandable that in the embodiments of the present application, a spatio-temporal longitude and latitude fusion device integrates the images, pathological sections, and clinical data of the patient throughout the diagnosis and treatment process, accurately positioning the dynamic state of the tumor in the time axis and spatial dimension; the mutation probability prediction module screens key features from a large amount of omics data to predict the gene mutation risk of tumor cells at key evolution stages such as recurrence and drug resistance; the multi-task learning framework breaks through the information barriers of images, genes, and clinical data in the neural network, and through sharing underlying features and differentiating upper-layer outputs, realizes the collaborative optimization of multiple tasks, dynamically tracks the tumor progression, early warns of mutation risks, and improves the accuracy of tumor prediction.

[0052] It should be noted that the spatio-temporal longitude and latitude fusion device integrates the CT images, pathological sections, and clinical test report data of the patient from the initial diagnosis to the follow-up visit, and uses image recognition algorithms to clearly label the size, location, and morphology of the tumor at each time node, as well as its distribution among different organs and tissues, accurately positioning the dynamic change state of the tumor in the time axis and spatial dimension.

[0053] Image recognition algorithm formula: Among them, is the comprehensive difference measure in the spatio-temporal domain; is the spatial coordinate; is the time dimension; is the feature change amount in the spatial ( ) or time ( ) direction; is the finally output feature representation; is the activation function; is the G term with a weight coefficient of 1, directly taking the spatio-temporal difference calculated by the first formula as the feature input; is the weight coefficient, balancing the contributions of G and the normalized attention features; is the batch normalization; is the feature output by the attention mechanism.

[0054] The mutation probability prediction module screens out the key features closely related to gene mutations from the patient's gene sequencing data, tumor tissue sample information, and past treatment record materials, combines the biological characteristics of tumor cells at specific evolution stages such as recurrence, metastasis, and drug resistance, and predicts the possibility of future gene mutations of tumor cells by analyzing the change trends of these key features, helping doctors judge the disease development in advance and formulate more effective treatment strategies.

[0055] For example, in the medical field, in the management of a non-small cell lung cancer patient who has received 9 months of targeted therapy, the mutation probability prediction module is deeply involved. This module first collects multi-dimensional information such as the patient's gene sequencing data, previous CT imaging materials, dynamic change curves of tumor markers, immune function test reports, and detailed targeted therapy medication history. Key features highly related to gene mutations are extracted from them, including the abundance change of the EGFR-T790M mutant gene in plasma, the fluctuation of the expression level of the immune checkpoint protein PD-L1 in tumor tissues, the degree of imbalance in the proportion of the patient's lymphocyte subsets, and the regression rate of tumor volume during the treatment process. Considering that the patient is currently in a critical evolution stage of slow tumor progression after osimertinib treatment, the module predicts that the probability of developing EGFR-C797S secondary drug resistance mutation is as high as 82% through systematic analysis and comprehensive evaluation of these key features, and at the same time indicates the potential risk of metastasis to bone tissue. Based on this prediction result, the medical team timely adjusts the treatment strategy, initiates the combined regimen of anti-angiogenic drugs and immunotherapy in advance, and strengthens bone density monitoring and preventive intervention measures, effectively delaying the disease progression and significantly improving the patient's quality of life and prognosis.

[0056] In the embodiment of the present application, the verification and clinical transformation module 500 includes, as Figure 7 shown, a pathway verification module, a clinical database docking interface, and a treatment recommendation engine.

[0057] Among them, the pathway verification module is used to verify the relationship of the predicted pathway in the occurrence and development of tumors, ensuring the authenticity and clinical translatability of the mutation prediction mechanism; the clinical database docking interface is used to integrate the clinical data of the TCGA and COSMIC databases to generate a mutation-treatment response association map; the treatment recommendation engine is used to automatically generate a personalized treatment plan and adjust it in real time in combination with the patient's feedback.

[0058] It can be understood that in the embodiments of the present application, the passage verification module observes the growth changes of tumor cells under different conditions through cell culture experiments, verifies the association between specific passages and mutations, simulates the tumor exponential growth changes in the exponential growth model, and ensures that the mutation prediction theory can withstand clinical tests; the clinical database docking interface deeply integrates and analyzes the gene mutation data, treatment plans, and efficacy feedback of tens of thousands of patients in authoritative databases such as TCGA and COSMIC, generates a visual mutation-treatment response association map, and intuitively presents the effective treatment means corresponding to different mutation types; the treatment recommendation engine generates a personalized treatment plan based on the association map, combined with the patient's gene test results, past medical history, and physical tolerance information, and dynamically optimizes and adjusts according to the changes in tumor indicators and adverse reactions after the patient takes the medicine. This makes the tumor research results more clinically valuable, helps doctors break through the limitations of experience, provides accurate and efficient treatment plans for patients, and improves the success rate of tumor treatment and the quality of life of patients.

[0059] Exponential growth model formula: Where, is the tumor volume; is the initial tumor volume; is the growth rate constant; is the time; is the natural constant.

[0060] For example, in the early growth monitoring of tumors, the exponential growth model can be used. For example, for lung cancer patients, the volume of pulmonary nodules is measured by regular (such as every 2 weeks) CT scans. Assume that the nodule volume at the initial discovery. After research, the growth rate constant k of this type of lung cancer cells without intervention is 0.03 / week. Substitute it into the exponential growth model , and the subsequent volume changes can be predicted. If not treated after 8 weeks, theoretically the nodule volume . Doctors can combine the actual monitoring data to judge whether the tumor growth conforms to the model expectations, assist in evaluating the disease development, and adjust the treatment plan in a timely manner. If the actual volume is much larger than the prediction, it may indicate a high degree of malignancy of the tumor or other growth-promoting factors, and more active intervention is needed.

[0061] The tumor mutation prediction system proposed in the embodiment of the present application integrates single-cell sequencing, spatial omics and imaging data through the data perception and intelligent analysis module, realizes multi-dimensional feature analysis and cross-modal data fusion, and comprehensively captures tumor heterogeneity; dynamically captures complex data associations and optimizes feature weights with the help of the intelligent association regulation engine, and improves the adaptability of the model to the dynamic changes of tumor data; uses the biological mechanism analysis module to simulate molecular behavior and mutation propagation paths to enhance the biological interpretability of the prediction results; integrates spatiotemporal features and neural networks through the multi-dimensional collaborative prediction module to achieve accurate mutation type prediction; and finally, it is verified that the clinical database is connected with the clinical transformation module to generate personalized treatment recommendations, forming a full-chain closed loop, and improving the accuracy, interpretability and clinical practical value of tumor mutation prediction. Thus, the problems of low mutation recognition accuracy and insufficient data integration in the prior art are solved.

[0062] A tumor mutation prediction system will be described below through a specific embodiment. Figure 8 As shown, including: After the patient is admitted to the hospital, the system automatically connects to the hospital's testing equipment through the Internet of Things. After completing single-cell sequencing and spatial omics testing, the system immediately obtains cell gene expression data, cell type annotations, and tissue section gene expression data with spatial coordinates. At the same time, chest CT plain scans and enhanced images are retrieved from the imaging system. The data collector preprocesses the raw data, such as cleaning up about 15% of invalid data in single-cell sequencing and unifying display parameters such as CT image window width (400HU) and window level (40HU). Subsequently, the system deeply analyzes the data: it identifies changes in five highly tumor-related genes such as TP53 and KRAS from single-cell data, analyzes gene expression differences and cell distribution characteristics in different regions from spatial omics data, and uses image analysis technology to extract features such as the size and shape of tumors in CT images, and cross-compares gene and imaging data to explore potential associations.

[0063] The system first analyzes the correlation between all data features and tumor gene mutations, and selects key features with a Pearson correlation coefficient of >0.7, such as EGFR gene expression (correlation coefficient 0.82), lobulation index of tumor imaging (correlation coefficient 0.78), etc. Then, the system simulates the natural evolution process to optimize the combination of these key features. After 100 iterations, it retains the feature combination that better reflects the characteristics of the tumor and improves the ability to identify mutations. When integrating different types of data, the system dynamically adjusts their weights according to the importance of each type of data in the analysis process. The weight of genetic data is increased to 60%, and the weight of imaging data is 40%, so that more valuable data for tumor mutation analysis can play a greater role.

[0064] Taking the EGFR gene mutation as an example, the system first obtains the three-dimensional structure of the relevant protein, simulates the binding process of the protein and the drug in the normal and mutant states, and visually presents that the mutation causes the binding energy to decrease from -8.2 kcal / mol to -5.1 kcal / mol, significantly affecting the interaction between the drug and the protein. At the same time, it is detected that the growth rate of tumor cells increases by 40% and the metastatic ability increases by 35% after the mutation, and the impact of the mutation on the tumor is evaluated. In addition, the system samples and detects multiple regions of the tumor tissue, traces the starting position of the gene mutation, draws a propagation path map of the mutation in the tumor, and combines the surrounding environment and cell characteristics of the tumor to predict the possible diffusion direction and influence range of the mutation in the future.

[0065] The system integrates the data of each stage of the patient from diagnosis to treatment, combines spatial omics and imaging data to determine the location and distribution of the tumor in the body, and analyzes the tumor size and gene change trends at different time points. By synthesizing the information in the time and space dimensions, the system constructs a tumor development state model. Based on this model, the system extracts key information and predicts the possibility of gene mutation in tumor cells at specific stages of subsequent treatment or disease development. For example, the probability of the emergence of the C797S drug resistance mutation 12 months after the patient uses osimertinib is predicted to be 32%, providing a basis for early intervention.

[0066] For the predicted tumor mutation-related path, the system first judges its rationality through bioinformatics analysis, then cultures mutant cells in the laboratory and conducts animal experiments to observe the actual impact of the mutation on the functions of tumor cells and tumor growth, ensuring the authenticity and reliability of the prediction results. The system connects to an authoritative clinical database, aggregates the gene mutation, treatment plan, and effect data of 1000 lung cancer patients, and generates visual charts to display the effective treatment methods corresponding to different mutations. Finally, the system automatically generates a personalized treatment plan according to the specific condition of the patient and the database information, and adjusts the plan in a timely manner according to the feedback such as the treatment effect and physical reaction of the patient during the treatment process to improve the treatment effectiveness.

[0067] In summary, in the embodiments of the present application, invalid data is eliminated through advanced data preprocessing to ensure the reliability of the analysis basis. In the data mining process, key features are screened, the feature combination is optimized by combining the simulated natural evolution algorithm, and the weights are dynamically allocated according to the data value, significantly improving the accuracy and sensitivity of tumor mutation identification. For specific mutation types, with the help of molecular dynamics simulation, the impact of mutations on drug binding and tumor biological behavior is analyzed at the microscopic level, providing an intuitive basis for mechanism research. The tumor development state model constructed based on the time and space dimensions can not only predict the probability of drug resistance mutations at specific treatment stages, but also trace the origin of mutations and deduce the diffusion path, providing forward-looking guidance for early intervention. In addition, through cross-validation of bioinformatics analysis, laboratory verification, and clinical big data, personalized treatment plans are generated and dynamically optimized, forming a complete closed loop from data collection, analysis and prediction to precise treatment, effectively improving the scientificity, precision and effectiveness of tumor diagnosis and treatment, and providing more targeted individualized medical solutions for tumor patients.

[0068] Next, a tumor mutation prediction method according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0069] As Figure 9 shown, the tumor mutation prediction method includes the following steps: In step S101, target data is obtained.

[0070] Among them, the target data includes key data such as single-cell sequencing, spatial omics, and imaging of patients.

[0071] It can be understood that in the embodiments of the present application, by integrating the diagnosis and treatment data of hospital patients, key features are identified to improve the comprehensiveness and accuracy of data analysis.

[0072] In step S102, key feature identification and analysis are performed based on the target data, complex associations of the data are captured according to the key features, feature association learning is performed and the weights are dynamically adjusted to generate an optimized feature vector. Based on the optimized feature vector, the biomolecular diffusion law of gene mutation is simulated to identify potential propagation paths and influencing factors. Based on the propagation paths and influencing factors, the mutation type and clinical phenotype are predicted to generate a prediction result; Among them, the biomolecular diffusion law of gene mutation refers to the dynamic law that biomolecules related to gene mutation migrate in space, change in distribution, and interact with each other in an organism or a specific environment, following certain physical, chemical, and biological mechanisms.

[0073] It is understandable that the embodiments of the present application depict the dynamic migration and interaction processes of relevant biomolecules in the spatio-temporal dimension, reveal the potential transmission paths and influencing factors of gene mutations, effectively connect the key features of target data with the complex relationships between clinical phenotypes, assist in formulating early intervention and treatment strategies, and improve the scientificity and effectiveness of medical decisions.

[0074] It should be noted that according to the optimized feature vectors, the diffusion law of biomolecules of gene mutations is simulated to identify potential transmission paths and influencing factors. The algorithm formula is as follows: ∂u / ∂t = D∇²u + f(u, v, t) u is the concentration or distribution of biomolecules; ∂u / ∂t is the change rate of biomolecule concentration over time; D is the diffusion coefficient; ∇²u is the Laplace operator; f(u, v, t) is the reaction function; v is other relevant variables; t is time.

[0075] In step S103, according to the prediction results and combined with the clinical data in the database, it is transformed into clinical treatment suggestions, and the treatment feedback of patients is integrated in real time through the neuron-level meta-reinforcement learning algorithm to dynamically optimize the personalized treatment plan.

[0076] Among them, the neuron-level meta-reinforcement learning algorithm is a reinforcement learning method algorithm that realizes rapid adaptation to new tasks or environmental changes under the meta-learning framework by simulating the synaptic plasticity mechanism between biological neurons.

[0077] It is understandable that the embodiments of the present application deeply mine the personalized characteristics of patients. For example, based on gene and physiological index data, the connection weights of neurons are dynamically adjusted to simulate the learning process of the brain, realizing the precise adaptation of the treatment plan. At the same time, it accelerates the generalization and migration of the model. When the patient's condition mutates or new treatment needs arise, the past learning experience can be reused to quickly generate effective strategies, significantly shortening the plan optimization cycle compared with traditional algorithms. In addition, this algorithm can also strengthen the dynamic feedback processing, integrate in real time the feedback such as index fluctuations and adverse reactions after the patient's treatment, transmit value signals through a mechanism similar to neurotransmitter transmission, efficiently optimize the decision-making network, and improve the long-term effectiveness and safety of the treatment plan.

[0078] It should be noted that the neuron-level meta-reinforcement learning algorithm formula: Among them, is the meta-learner; is the traditional gradient of the neuron weights; is the historical gradient time series state; is the update amount of the weights.

[0079] For example, in the personalized formulation of tumor chemotherapy regimens, the neuron-level meta-reinforcement learning algorithm simulates the plasticity of neuron synapses, encoding clinical information such as the patient's gene sequencing data, tumor marker levels, and previous chemotherapy responses as neuron inputs. By adjusting the connection weights of neurons, the algorithm learns the mapping relationship between different treatment regimens and the patient's treatment efficacy. For example, in the treatment of breast cancer patients, according to feedback such as the degree of tumor shrinkage and blood toxicity indicators after medication, the synaptic weight update strategy is quickly adjusted. When faced with new situations such as drug-resistant mutations, the past treatment experience is reused through the meta-learning framework to dynamically optimize the combination, dosage, and cycle of chemotherapy drugs, reducing the risk of adverse reactions while improving the treatment effect and accurately adapting to the individual differences of patients.

[0080] A tumor mutation prediction method proposed according to an embodiment of the present application integrates single-cell sequencing, spatial omics, and imaging data through a data perception and intelligent analysis module to achieve multi-dimensional feature analysis and cross-modal data fusion, comprehensively capturing tumor heterogeneity; dynamically capturing complex data associations and optimizing feature weights with an intelligent association and regulation engine to improve the adaptability of the model to the dynamic changes of tumor data; using a biological mechanism analysis module to simulate molecular behavior and mutation propagation paths to enhance the biological interpretability of prediction results; achieving accurate mutation type prediction through a multi-dimensional collaborative prediction module that fuses spatio-temporal features and neural networks; and finally docking with a clinical database through a verification and clinical transformation module to generate personalized treatment recommendations, forming a full-chain closed loop to improve the accuracy, interpretability, and clinical practical value of tumor mutation prediction. Thus, problems such as low accuracy of mutation identification and insufficient data integration in the prior art are solved.

[0081] A tumor mutation prediction method will be elaborated below through a specific embodiment, as Figure 10 shown, including: In a tumor hospital, multi-faceted data of 2,000 lung cancer patients were collected. Using next-generation sequencing, the mutation status of more than 100 gene loci in the patients' tumor tissues and peripheral blood, as well as gene copy number variation data, were obtained, including common lung cancer driver genes such as EGFR, ALK, and ROS1. At the same time, basic information such as the patients' age, gender, and smoking history was recorded, the tumor stage was recorded using the TNM staging standard, the tumor size and metastasis were statistically analyzed, and more than 50 clinical indicators such as blood routine, liver and kidney function were recorded. In addition, the chest CT images of the patients were saved, recording the location, shape, density, etc. of the tumor.

[0082] The collected data was sorted, the genetic data was standardized, and tumor features were extracted from CT images. After analysis, it was found that the deletion mutation of exon 19 of the EGFR gene and the fusion of the ALK gene were closely related to the patient's tumor stage and age. These important genetic, clinical, and imaging features were integrated, and the most critical 30 feature dimensions were selected to form an optimized feature vector, which can reduce the amount of data without losing important information.

[0083] Based on the optimized feature vector, the diffusion of biomolecules related to lung cancer gene mutations around the tumor is simulated. Taking into account factors such as tumor cell growth, angiogenesis, and environmental temperature and pH, the diffusion process of biomolecules over a period of time is simulated. Through simulation, it is found that, for example, EGFR mutant proteins will accumulate in large quantities around tumor blood vessels, promoting tumor growth and metastasis. Based on these findings, combined with the previous feature vectors, the types of mutations that may occur in patients in the next 3-6 months, as well as disease progression and metastasis risks, are predicted with an accuracy rate of 82%.

[0084] The prediction results are compared with the treatment records of more than 100,000 patients in the hospital database, and the successful treatment methods of similar cases are referred to. Combined with clinical guidelines, personalized treatment recommendations are made for patients. For example, if it is predicted that a patient may have an EGFR-T790M drug-resistant mutation, it is recommended to use anti-angiogenic drugs in combination with current targeted therapy. During the treatment process, the treatment plan is adjusted every two weeks based on feedback such as changes in the patient's tumor markers, changes in tumor size, and quality of life scores. Actual applications have shown that the average time without disease progression for patients treated with this method has been extended from 8 months to 11 months, and the treatment effect has been significantly improved.

[0085] In summary, through multi-dimensional data integration and in-depth analysis, the embodiments of this application have brought breakthrough beneficial effects to the diagnosis and treatment of lung cancer. At the data level, a total of 2,000 patients' multi-source information covering genes, clinical conditions, and images has been comprehensively collected, constructing a huge and detailed database, laying a solid foundation for subsequent analysis. In the feature mining link, by accurately sorting out key features and generating optimized feature vectors, complex data has been successfully streamlined into core elements, significantly improving the data processing efficiency and analysis accuracy. Based on this, the law of biomolecular diffusion is simulated, and tumor mutations and disease progression are predicted with a high accuracy of 82%, enabling doctors to gain an insight into the disease evolution trend in advance. In the formulation of treatment plans, combined with more than 100,000 clinical records in the hospital and authoritative guidelines, personalized plans are customized, and according to the real-time feedback of tumor markers, tumor size, and quality of life during the patient's treatment process, the treatment strategy is dynamically optimized every two weeks. Finally, this method has significantly extended the average time without disease progression of patients from 8 months to 11 months, not only effectively delaying the disease development, but also significantly reducing the risk of disease recurrence and metastasis, greatly improving the quality of life of patients. At the same time, it provides a practical example for tumor precision medicine, promoting the diagnosis and treatment of lung cancer towards a more scientific, efficient, and personalized direction.

[0086] Figure 11 It is a schematic structural diagram of an electronic device provided by an embodiment of this application. The electronic device may include: A memory 1101, a processor 1102, and a computer program stored on the memory 1101 and executable on the processor 1102.

[0087] When the processor 1102 executes the program, it implements a tumor mutation prediction method provided in the above embodiment.

[0088] Furthermore, the electronic device further includes: A communication interface 1103 for communication between the memory 1101 and the processor 1102.

[0089] The memory 1101 is used to store a computer program executable on the processor 1102.

[0090] The memory 1101 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0091] If the memory 1101, the processor 1102, and the communication interface 1103 are implemented independently, the communication interface 1103, the memory 1101, and the processor 1102 can be interconnected via a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 11 only a thick line is used in Figure 11 , but it does not mean that there is only one bus or one type of bus.

[0092] Optionally, in a specific implementation, if the memory 1101, the processor 1102, and the communication interface 1103 are integrated on a single chip, the memory 1101, the processor 1102, and the communication interface 1103 can communicate with each other through an internal interface.

[0093] The processor 1102 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0094] The embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, a tumor mutation prediction method as described above is implemented.

[0095] In addition, the embodiments of the present application further provide a computer program product, including a computer program or instruction, and when the computer program or instruction is executed, a tumor mutation prediction method as described above is implemented.

[0096] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0097] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0098] Any process or method description shown in a flowchart or described otherwise herein may be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of the present application includes additional implementations in which functions may be executed in a substantially simultaneous manner or in an order opposite to that shown or discussed, according to the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0099] It should be understood that the various parts of the present application may be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods may be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art may be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0100] Those of ordinary skill in the art of the present technology can understand that all or part of the steps carried by the method of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0101] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A tumor mutation prediction system, characterized in that, include: Data perception and intelligent analysis module, intelligent correlation regulation engine, biological mechanism analysis module, multi-dimensional collaborative prediction module, verification and clinical transformation module; among them, The data perception and intelligent analysis module is used to integrate single-cell sequencing, spatial omics and imaging data, identify data features, and perform analysis and processing; The intelligent correlation control engine is used to capture the complex correlation between tumor data, perform feature correlation learning and dynamically adjust weights; The biological mechanism analysis module is used to simulate the dynamic behavior process of biological molecules and identify the potential propagation path of gene mutations and their impact on biological functions; The multi-dimensional collaborative prediction module is used to integrate the spatial location and time series characteristics of tumor data and combine with neural networks to accurately predict the tumor gene mutation type; The verification and clinical transformation module is used to analyze and verify the prediction results, perform clinical application transformation of tumor mutations, and generate treatment recommendations in combination with the database.

2. The tumor mutation prediction system according to claim 1, wherein The data perception and intelligent analysis module includes a cross-source data collector and a feature cube identifier. The cross-source data collector is used to capture single-cell sequencing and spatial omics data in real time, and combines the Internet of Things to perform data processing and transmission; the feature cube identifier is used to identify key feature information in the data, perform multi-dimensional rotation and combination analysis, and mine hidden features of data such as pathological images and genomes.

3. The tumor mutation prediction system according to claim 1, characterized in that, The intelligent association regulation engine includes a key feature selector, a feature evolution learning cabin, and a dynamic weight optimization module, wherein the key feature selector is used to screen out key features that are highly correlated with tumor gene mutations and have strong correlations; the feature evolution learning cabin is used to simulate natural selection and genetic algorithms, optimize features, adapt to constantly changing tumor data, and improve feature expression capabilities and discrimination; the dynamic weight optimization module is used to dynamically adjust the weight of data in the model.

4. The tumor mutation prediction system according to claim 1, wherein The biological mechanism analysis module includes a biomolecule simulator and a mutation meteor tracker, wherein the biomolecule simulator is used to simulate the motion trajectory of molecular interactions, analyze the change rules in a visual way, and quantify the impact of mutations on tumor phenotypes; the mutation meteor tracker is used to track the propagation path of tumor gene mutations in real time, quickly locate the origin point of the mutation, and at the same time, predict its propagation direction and impact range.

5. The tumor mutation prediction system according to claim 1, characterized in that The multi-dimensional collaborative prediction module includes a space-time longitude and latitude fuser and a mutation probability prediction module, wherein the space-time longitude and latitude fuser is used to locate the state of the tumor in different time and space dimensions; the mutation probability prediction module is used to extract key feature information and predict the probability of gene mutation of tumor cells at a specific evolutionary stage.

6. The tumor mutation prediction system according to claim 1, wherein The verification and clinical transformation module includes a pathway verification module, a clinical database docking interface and a treatment suggestion engine, wherein the pathway verification module is used to verify the relationship between the predicted pathway and the occurrence and development of tumors, and ensure the authenticity and clinical transformability of the mutation prediction mechanism; the clinical database docking interface is used to integrate clinical data from the TCGA and COSMIC databases to generate a mutation-treatment response association map; the treatment suggestion engine is used to automatically generate personalized treatment plans, and make real-time adjustments based on patient feedback.

7. A method for predicting tumor mutations, characterized in that, The method comprises: Obtain target data; Based on the target data, conduct key feature recognition and analysis, capture the complex associations of the data according to the key features, conduct feature association learning and dynamically adjust the weights to generate an optimized feature vector. According to the optimized feature vector, simulate the biomolecular diffusion law of gene mutation, identify potential transmission paths and influencing factors, and based on the transmission paths and influencing factors, predict the mutation type and clinical phenotype to generate a prediction result; Based on the prediction result and combined with the clinical data in the database, convert it into clinical treatment suggestions, and dynamically optimize the personalized treatment plan by integrating the patient's treatment feedback in real time through a reinforcement learning algorithm.

8. An electronic device, characterized in that, Comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the tumor mutation prediction method according to claim 7.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instruction is executed, it implements a tumor mutation prediction method according to claim 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instruction is executed, it implements a tumor mutation prediction method according to claim 7.

Images