A deep learning-based viral genomic data analysis and prediction system

Abstract

The application discloses a virus genome data analysis and prediction system based on deep learning, relates to the technical field of data management, and extracts local features of a genome and realizes classification and variation detection by using a convolutional neural network through a deep learning model module; a robustness evaluation module quantifies the performance fluctuation of the model in new mutations by analyzing a new mutation test data set; a confidence analysis module evaluates the abnormality of the model prediction confidence distribution; a comprehensive analysis module generates a comprehensive stability score in combination with the robustness and confidence results, dynamically quantifies the stability of the model to new mutations; and a model optimization module iteratively updates the model architecture and training data set based on the analysis results, thereby significantly improving the prediction performance of the model on dynamic variation of viruses, effectively solving the problem that existing models are difficult to adapt to the cumulative drift of virus genome mutations, providing an efficient and stable solution for rapid detection of new mutations, and having important public health significance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data management, in particular to a virus genome data analysis and prediction system based on deep learning. BACKGROUND

[0002] Virus genome data analysis and prediction based on deep learning refers to using deep learning algorithms to extract information from virus genome data, conduct pattern recognition and function prediction, and reveal the characteristics, evolutionary path or transmission mode of viruses. This method relies on the powerful computing power of deep learning and the ability to model complex nonlinear relationships, and can find features in large-scale, high-dimensional genome data that are difficult to identify through traditional methods.

[0003] For example, in the prior art, researchers use convolutional neural networks (CNN) to classify and detect mutations in viral genome sequences. CNN can automatically extract features from sequence data, distinguish differences between different virus strains, and help predict the transmission trend of viruses. In addition, recurrent neural networks (RNN) are used to analyze viral RNA sequences and predict gene expression regulation functions, such as changes in the function of spike protein. Another application is to use generative adversarial networks (GAN) to predict the potential impact of viral mutations, providing a reference for vaccine design. These methods improve analysis efficiency and can cope with the high variability of viral genomes.

[0004] The prior art has the following shortcomings:

[0005] Virus variation is a dynamic, time-dependent process. CNN models are usually trained based on static data sets and cannot adapt to the gradual accumulation and drift of mutations in viral genomes. Over time, the predictive performance of the model for new mutations will gradually decrease, and this erosion can seriously affect the actual application effect of the model. In addition, inaccurate prediction of new mutations (especially functional mutations) can lead to missing high-risk mutations, such as those that cause higher transmissibility, immune escape or drug resistance. Thus, it is unable to identify new virus strains that are resistant to vaccines or treatments in a timely manner, directly affecting the timeliness of public health policy formulation and vaccine updates. SUMMARY

[0006] The purpose of the present application is to provide a virus genome data analysis and prediction system based on deep learning to solve the problems in the background art.

[0007] In order to achieve the above-mentioned purpose, the present application provides the following technical scheme: a virus genome data analysis and prediction system based on deep learning, comprising a data processing module, a deep learning model module, a robustness evaluation module, a confidence analysis module, a comprehensive analysis module and a model optimization module.

[0008] a data processing module for collecting, preprocessing and encoding viral genome sequence data, including converting the genome sequence into a numerical form for deep learning model processing through One-hot encoding and numerical mapping;

[0009] a deep learning model module including a convolutional neural network for extracting local features from the encoded viral genome sequence and performing classification and mutation detection;

[0010] a robustness evaluation module for constructing a new mutation test dataset containing non-standard mutation forms, and evaluating the robustness of the convolutional neural network model in predicting new mutations according to the fluctuation range of the performance drop ratio between the benchmark model performance and the new mutation dataset performance;

[0011] a confidence analysis module for collecting the prediction confidence distribution of the convolutional neural network model on the new mutation test dataset, and analyzing the average confidence deviation between correct and incorrect prediction samples to evaluate the abnormality degree of the confidence distribution of the convolutional neural network model;

[0012] a comprehensive analysis module for combining the robustness of the convolutional neural network model in predicting new mutations and the abnormality degree of the confidence distribution of the convolutional neural network model to generate a comprehensive stability score of the convolutional neural network model and determine the stability of the convolutional neural network model in predicting new mutations;

[0013] a model optimization module for updating the architecture and training dataset of the convolutional neural network model based on the results of the comprehensive analysis module to improve the prediction performance of the convolutional neural network model on new mutations.

[0014] Preferably, in the robustness evaluation module, a drop ratio fluctuation index is generated after analyzing the fluctuation range of the performance drop ratio between the benchmark model performance and the new mutation dataset performance, and the drop ratio fluctuation index is obtained by:

[0015] collecting the performance drop ratio of all test samples from the evaluation experiment , forming a dataset: ; wherein N is the total number of mutation samples, the mean value of the performance drop ratio is calculated as , the calculation formula is: ; the variance of the performance drop ratio is calculated as , the calculation formula is: ; the drop ratio fluctuation index is calculated to quantify the performance fluctuation degree of the model on new mutation samples, and the expression is: ; wherein MK is the drop ratio fluctuation index.

[0016] Preferably, in the confidence analysis module, the average confidence deviation index is generated after analyzing the average confidence deviation of correct prediction samples and error prediction samples, and the average confidence deviation index is obtained by the following method:

[0017] From the prediction results of the convolutional neural network model on the new mutation test dataset, the prediction confidence of each sample is extracted ; the data is divided into two groups: correct prediction group: the confidence set of samples predicted correctly by the model ; error prediction group: the confidence set of samples predicted incorrectly by the model ; for each group of confidence data, calculate its mean and standard deviation σ, and skewness, the calculation expression of skewness is: ; S is the skewness value; calculate kurtosis, which measures the degree of peak of confidence distribution, the expression is: ; K is the kurtosis value, based on the skewness and kurtosis values, define the average confidence deviation index, the expression is: ; wherein, is the average confidence deviation index.

[0018] Preferably, in the comprehensive analysis module, the comprehensive stability score of the convolutional neural network model is generated by combining the robustness of the convolutional neural network model in new mutation prediction and the abnormal degree of the confidence distribution of the convolutional neural network model, specifically:

[0019] Convert the decline ratio fluctuation index and the average confidence deviation index into a comprehensive feature vector, and use the comprehensive feature vector as the input of the machine learning model. The machine learning model takes the comprehensive stability score value label of each group of comprehensive feature vectors as the prediction target, and takes minimizing the sum of prediction errors of the comprehensive stability score value labels of all convolutional neural network models as the training target. Train the machine learning model until the sum of prediction errors converges, and then stop the model training. According to the output result of the model, the comprehensive stability score value of the convolutional neural network model is determined, wherein the machine learning model is a polynomial regression model.

[0020] Preferably, the comprehensive stability score value of the convolutional neural network model obtained is compared with the preset stability threshold value according to historical data. If the comprehensive stability score value of the convolutional neural network model is greater than or equal to the preset stability threshold value, it means that the convolutional neural network model is stable in new mutation prediction, and no warning signal is generated at this time. If the comprehensive stability score value of the convolutional neural network model is less than the preset stability threshold value, it means that the convolutional neural network model is unstable in new mutation prediction, and a warning signal is generated at this time.

[0021] Preferably, in the model optimization module, based on the results of the comprehensive analysis module, the architecture of the convolutional neural network model and the training data set are updated, specifically:

[0022] The architecture parameters Θ of the optimized CNN model are optimized to minimize the instability of the model, and the target formula is: ; In the formula, Θ is the architecture parameter of the CNN model, MK(Θ) is the drop rate fluctuation index under the current architecture, MCBI(Θ) is the average confidence bias index under the current architecture, and α and β are weight parameters; The model architecture is dynamically optimized using an architecture search algorithm: search for the convolution kernel size K, the number of layers L, and the number of channels C: ;

[0023] Multi-scale convolution and regularization mechanism are introduced to enhance the adaptability to new mutations: multi-scale convolution The expression is: ; Different scale convolution kernels are represented, and the regularization term is: ; is the regularization term, which controls the model complexity and avoids overfitting, The total loss function is represented, which is the optimization target of the model, The task loss of the model in a specific task is represented, and λ is the weight coefficient of the regularization strength.

[0024] Preferably, the proportion of new mutation samples in the data set is enhanced, and the target formula is: ; D is the current training data set, MK(D) is the drop rate fluctuation index under the data set D, MCBI(D) is the average confidence bias index under the data set D, and γ and δ are weight parameters, is the optimized training data set;

[0025] Increase the proportion of newly collected real new mutation samples : ; is the new mutation sequence collected from the public database, is the original new mutation sample, is the real mutation sample; Simulated new mutation samples are generated by using mutation patterns : ; The samples are simulated mutation operations according to the probability p;

[0026] By adjusting the weight of the new mutation sample in the training set, the data distribution is optimized: ; is the weight of the i-th sample, is the number of i-th samples, is the total number of training set samples, the total number of classes;

[0027] In combination with the dynamic updating strategy of architecture and dataset, the comprehensive stability score of the model is optimized, and the target is optimized: ; update the dataset D, minimize MK and MCBI: ; update the architecture , enhance the adaptability of the model to new mutations: ; wherein, is the current training dataset, is the updated training dataset, containing original data and new data; is the current model architecture parameter, and ΔΘ is the architecture adjustment amount, is the updated architecture parameter.

[0028] In the above technical solution, the technical effects and advantages provided by the present application are:

[0029] 1、The present application realizes efficient analysis of dynamic variation of viral genome by combining data processing, deep learning modeling, robustness evaluation, confidence analysis, comprehensive analysis and model optimization six modules. Through dynamic updating of dataset and model architecture, the system can adapt to the accumulation and drift of new mutations in real time, improve the detection ability of high-risk mutations, and provide more timely and accurate support for vaccine design and treatment plan adjustment. Especially through the quantitative indicators of the robustness evaluation module and the confidence analysis module, the system can accurately evaluate the stability and prediction reliability of the model, and generate a comprehensive stability score through the comprehensive analysis module, so as to realize the stability monitoring and optimization of the model.

[0030] 2、The present application introduces multi-scale convolution and regularization mechanism, and the system enhances the extraction ability of new mutation characteristics; by increasing real and simulated mutation samples and adjusting sample weight, the system optimizes the training data distribution. This flexible and comprehensive optimization scheme significantly improves the performance and stability of the model in new mutation prediction, reduces the impact of inaccurate mutation prediction on public health decision-making, provides reliable technical support for virus transmission monitoring and prevention and control, and has important practical application value. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0032] Figure 1 is the system module diagram of the present application. DETAILED DESCRIPTION

[0033] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0034] Embodiments, please refer to Figure 1 As shown in the accompanying drawings, the virus genomic data analysis and prediction system based on deep learning described in the embodiments includes a data processing module, a deep learning model module, a robustness evaluation module, a confidence analysis module, a comprehensive analysis module, and a model optimization module.

[0035] The data processing module is used for collecting, preprocessing and encoding the viral genomic sequence data, including converting the genomic sequence into a numerical form for deep learning model processing through One-hot encoding and numerical mapping.

[0036] The deep learning model module includes a convolutional neural network, which is used to extract local features from the encoded viral genomic sequence and perform classification and mutation detection.

[0037] The robustness evaluation module is used to construct a new mutation test data set containing non-standard mutation forms, and evaluate the robustness of the convolutional neural network model in predicting new mutations according to the fluctuation amplitude of the decline ratio between the performance of the benchmark model and the performance of the new mutation data set.

[0038] The confidence analysis module is used to collect the prediction confidence distribution of the convolutional neural network model on the new mutation test data set, and analyze the average confidence deviation of correct and incorrect prediction samples to evaluate the abnormality degree of the confidence distribution of the convolutional neural network model.

[0039] The comprehensive analysis module is used to combine the robustness of the convolutional neural network model in predicting new mutations and the abnormality degree of the confidence distribution of the convolutional neural network model to generate a comprehensive stability score of the convolutional neural network model, and determine the stability of the convolutional neural network model in predicting new mutations.

[0040] The model optimization module is used to update the architecture and training data set of the convolutional neural network model based on the results of the comprehensive analysis module to improve the prediction performance of the convolutional neural network model for new mutations.

[0041] In the data processing module, viral genome sequence data is collected from public or private databases, ensuring diversity and coverage of data sources. Sequences are obtained from virus databases (such as GISAID, NCBI GenBank) through API or bulk download tools. Typical formats are FASTA or CSV files, where genome sequences are represented as strings (e.g. "ATCG...GCTA"). Metadata collection includes virus strain name, collection time, geographic location, and host information, providing support for subsequent analysis.

[0042] The collected raw genome sequence data is cleaned and standardized to ensure data integrity and consistency. Check for missing fragments or incomplete sequences in the data, complete or remove incomplete samples. Remove redundant duplicate sequences to avoid data bias. Use multi-sequence alignment algorithms (such as MAFFT, ClustalW) to align sequences, ensuring uniform reference coordinates for mutation sites. For sequences of different lengths, perform truncation or padding operations to make them uniform in length, such as fixing them at 30,000 bases (typical SARS-CoV-2 full genome length).

[0043] To convert viral genome sequences into numerical forms acceptable to deep learning models, the data processing module provides the following two encoding methods:

[0044] Each base (A, T, C, G) is represented by a unique four-dimensional vector, ensuring the uniqueness of each base. Define the one-hot vector representation of the base: A=[1,0,0,0],T=[0,1,0,0],C=[0,0,1,0],G=[0,0,0,1]; Convert one by one according to sequence position: Example: for the sequence "ATCG", its encoding result is: [[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]]; Generate high-dimensional tensor representation: Assuming the genome length is L, the shape of the encoded data is (L,4), which can be directly input into the CNN model.

[0045] Each base is mapped to a fixed numerical value to reduce the encoding dimension. Define the mapping rule: A=1, T=2, C=3, G=4; Convert one by one according to sequence position: Example: for the sequence "ATCG", its encoding result is: [1,2,3,4]; Use embedding layer (Embedding Layer) to map numerical values to continuous vectors to improve feature expression ability.

[0046] The encoded viral genome sequence is outputted in a standardized numerical tensor form as input for the deep learning model: One-hot encoding output shape: (Z, L, 4), where Z is the number of samples, L is the sequence length, and 4 is the encoding dimension. Numerical mapping output shape: (Z, L), where the numerical values can be further embedded into high-dimensional vectors.

[0047] The deep learning model module, including a convolutional neural network, is used to extract local features from the encoded viral genome sequence and perform classification and mutation detection.

[0048] The convolutional neural network model includes: an input layer that receives pre-processed and encoded viral genome data (such as One-hot encoding or numerical mapping tensors). Convolutional layers that extract local features such as base patterns, mutation sites, and repeat sequences through convolution operations. Pooling layers that reduce the dimensionality of feature maps, preserving important features and improving computational efficiency. Fully connected layers that integrate extracted local features for classifying virus strains or detecting mutation points. Output layer that outputs classification probabilities or mutation detection results.

[0049] Conserved region features: important functional regions in the viral genome that are less likely to mutate, such as gene fragments encoding key proteins (such as spike proteins). Base patterns are highly consistent, showing low variability. Commonly found in key functional regions of viruses, which may be related to viral replication, infection, or immune evasion. These consistent patterns are automatically captured by small convolutional kernels (such as size 3 or 5) and further analyzed downstream.

[0050] Mutation site features: base positions in the genome sequence that have mutated compared to the reference sequence, including point mutations, insertions, and deletions. Features include point mutations: substitution of a single base (e.g. A→G). Insertions: addition of additional base sequences (e.g. AGT→AGCT). Deletions: deletion of bases (e.g. AGT→AT). Use sliding convolution operations to detect these local changes and map mutation site features to specific activation patterns. Multi-channel convolution kernels can capture different types of variation patterns.

[0051] Repeat sequence features: regions in the genome composed of repeated arrangements of base fragments, which may be related to gene regulation or viral evolution. Features include short repeats: such as ATATAT. Long repeats: such as larger gene unit repeat fragments. Detect repeat patterns through convolution kernels, especially fixed-size repeat units (such as trinucleotide or hexanucleotide repeats). Provide a comprehensive description of the number and length of repeat regions.

[0052] Genome boundary features: Represent the boundaries between different functional regions of the genome (e.g., start and stop codons, non-coding region boundaries). Feature manifestation: Gene region boundaries often have specific base patterns, such as conserved sequences in promoter regions. Use local convolution to extract these boundary features, identifying the division of different regions of the genome.

[0053] Base frequency features: The frequency of occurrence of different bases (A, T, C, G) within a local window and their distribution patterns. Feature manifestation: Uneven base distribution can be associated with functional hotspots (e.g., enhancers or splice sites). Multi-channel convolution kernels combined with local pooling operations can extract base frequency distribution patterns within local windows.

[0054] Long-range dependency features (combined with improved architecture): Dependence relationships between distant locations in the genome sequence (e.g., sequence complementarity at both ends or long-range interactions). Feature manifestation: Common in complementary sequences in RNA secondary structures. CNN extension: Combine multi-scale convolution kernels or with structures such as Transformers to capture long-range dependency features.

[0055] Convolution layer design includes: Convolution kernel size: Use different sizes of convolution kernels (e.g., 3, 5, 7) to capture local features of different lengths. Small kernels are suitable for detecting variant sites and short repeat sequences, and large kernels are suitable for detecting long repeats and conserved regions. Number of convolution channels: Increase the number of convolution channels (e.g., 32, 64, 128) to extract more dimensional feature patterns.

[0056] Pooling layer design includes: Max pooling: Retains the most significant features within each convolution window, suitable for variant detection. Global pooling: Aggregates global features of the entire sequence for virus strain classification tasks. Output activation map of local features, shape (Q, L', C), where: Q is the number of samples, L' is the length of the features after pooling. C is the number of feature channels, representing the number of types of local features extracted.

[0057] Robustness evaluation module for constructing a new mutation test dataset containing non-standard mutation forms, evaluating the robustness of the convolutional neural network model for new mutation prediction performance according to the fluctuation amplitude of the decline ratio between the performance of the benchmark model and the performance of the new mutation dataset.

[0058] Constructing a new mutation test dataset, generating a test dataset containing real and simulated new mutation forms, covering as many non-standard mutations as possible that do not appear in the training set. Data types: Point mutations: Single base substitution (e.g., A→G). Insertion mutations: Insertion of additional base fragments (e.g., AGT→AGCT). Deletion mutations: Loss of base fragments (e.g., AGT→AT). Repeat mutations: Local repeat fragments (e.g., AT→ATAT). Large fragment variations: Involve insertion, deletion or rearrangement of large fragments in the genome (e.g., gene structure abnormalities).

[0059] The performance of the CNN model is evaluated on the mutation-free test dataset (i.e., the standard reference genome), and the benchmark performance indicators are recorded. Performance indicators: Classification task: accuracy, F1 score, ROC-AUC. Mutation detection task: sensitivity, specificity, mean absolute error (MAE).

[0060] Run the model on the new mutation test dataset, calculate the difference between the performance indicators and the benchmark performance, and quantify the model robustness, formula: ; where, is the performance drop ratio, is the benchmark performance, is the performance of the new mutation dataset.

[0061] After analyzing the fluctuation range of the drop ratio between the benchmark model performance and the performance of the new mutation dataset, the drop ratio fluctuation index is generated to evaluate the robustness of the convolutional neural network model for new mutation prediction performance. The method for obtaining the drop ratio fluctuation index is:

[0062] Collect the performance drop ratio of all test samples from the evaluation experiment , form a dataset: ; where, N is the total number of mutation samples, ensuring that all are valid values (no missing or abnormal values). Abnormal values exceeding ±3σ can be labeled or removed. Calculate the mean value of the performance drop ratio , the calculation formula is: ; Calculate the variance of the performance drop ratio , the calculation formula is: ; Calculate the drop ratio fluctuation index, which is used to quantify the performance fluctuation degree of the model on the new mutation sample, the expression is: ; where, MK is the drop ratio fluctuation index.

[0063] When the drop ratio fluctuation index is larger, it means that the prediction performance of the convolutional neural network model for new mutations fluctuates greatly, showing low robustness. This indicates that the adaptability of the model to different mutation types or samples is significantly different, and it may perform well on some mutation forms, but the performance drops sharply on other forms. This volatility will lead to inconsistency in the model's prediction results, making it difficult to effectively deal with complex mutation scenarios in practical applications, especially when it faces unseen mutation samples.

[0064] The smaller the drop ratio fluctuation index, the more stable the prediction performance of the convolutional neural network model on new mutations, indicating higher robustness. This means that the model can maintain consistent performance on various mutation types and provide more reliable prediction results for new mutations. A low fluctuation index reflects the model's advantages in adaptability and generalization, providing more robust support for mutation detection and classification tasks and performing well in handling diverse mutation data.

[0065] A confidence analysis module is used to collect the prediction confidence distribution of the convolutional neural network model on the new mutation test dataset and analyze the average confidence deviation between correct and incorrect prediction samples to evaluate the abnormality degree of the confidence distribution of the convolutional neural network model.

[0066] The prediction confidence of the CNN model refers to the probability value generated by the output layer (such as the Softmax activation function) for a certain prediction class, indicating the model's confidence in that prediction. For each test sample, the highest class probability (Top-1 probability) in the Softmax output is extracted as the prediction confidence. The confidence values of all test samples and their corresponding actual prediction results (correct or incorrect) are collected.

[0067] According to the prediction results of the test samples, the samples are divided into two groups: the correct prediction group: samples predicted by the model consistent with the actual label. The incorrect prediction group: samples predicted by the model inconsistent with the actual label. The average prediction confidence of the two groups of samples is calculated, and the average confidence difference between correct and incorrect prediction samples is calculated to quantify the deviation of the confidence distribution. The greater the deviation, the higher the confidence of the model for incorrect prediction samples, and the more significant the abnormality degree of the confidence distribution.

[0068] After analyzing the average confidence deviation of correct and incorrect prediction samples, the average confidence deviation index is generated to evaluate the abnormality degree of the confidence distribution of the convolutional neural network model. The method for obtaining the average confidence deviation index is:

[0069] From the prediction results of the convolutional neural network (CNN) model on the new mutation test dataset, the prediction confidence of each sample is extracted (the Top-1 probability of the Softmax output).

[0070] The data is divided into two groups: the correct prediction group: the confidence set of samples predicted correctly by the model ; the incorrect prediction group: the confidence set of samples predicted incorrectly by the model ; ensuring that there are no missing values or outliers (such as confidence values exceeding a reasonable range, usually ∈[0,1].

[0071] For each group of confidence data, calculate its mean and standard deviation σ, and skewness, which is an important indicator to measure the symmetry of confidence distribution, expressed as: ; S is the skewness value. S>0 means the distribution is right-skewed, with confidence concentrated at higher values, indicating that the model may be overly confident for some samples. S<0 means the distribution is left-skewed, with confidence concentrated at lower values, indicating that the model has low overall confidence. S=0 means the distribution is symmetric, with confidence evenly distributed. Calculate kurtosis, which measures the sharpness of the confidence distribution or the concentration of extreme values, expressed as: ; K is the kurtosis value, K>0 means the distribution is steep, with confidence concentrated at extreme values (abnormally high or abnormally low), indicating that the model confidence distribution is abnormal. K<0 means the distribution is flat, with confidence more dispersed, indicating that the model has low confidence. K=0 is a characteristic of normal distribution, with confidence distribution being normal, based on skewness and kurtosis values, define the average confidence bias index, expressed as: ; in the formula, is the average confidence bias index.

[0072] When the average confidence bias index (MCBI) is larger, it means that the convolutional neural network model has a higher degree of abnormal confidence distribution. This means that the model has a significant difference in confidence between correct and incorrect predictions, and may show excessive confidence (skewness value) for incorrect predictions, or the confidence distribution is too concentrated at extreme values (kurtosis value). This situation will lead to the unreliability of the model in practical application, such as excessive trust in incorrect prediction results, increasing the risk of decision-making.

[0073] When the average confidence bias index (MCBI) is smaller, it means that the convolutional neural network model has a lower degree of abnormal confidence distribution, and the confidence distribution of the prediction result is more reasonable. The model has a smaller difference in confidence between correct and incorrect predictions, and the confidence distribution is more uniform without excessive concentration at extreme values. This situation indicates that the model's prediction results are more stable, and the confidence better reflects the model's true ability, thus providing more reliable support for downstream decision-making.

[0074] The comprehensive analysis module is used to combine the robustness of the convolutional neural network model in predicting new mutations and the degree of abnormal confidence distribution of the convolutional neural network model to generate a comprehensive stability score of the convolutional neural network model, and determine the stability of the convolutional neural network model in predicting new mutations.

[0075] The descending ratio fluctuation index and the average confidence bias index are converted into a comprehensive feature vector, the comprehensive feature vector is taken as an input of a machine learning model, the machine learning model takes a comprehensive stability score value label of the convolutional neural network model as a prediction target, a sum of prediction errors of the comprehensive stability score value labels of all the convolutional neural network models is minimized as a training target, the machine learning model is trained until the sum of prediction errors converges, and the comprehensive stability score value of the convolutional neural network model is determined according to an output result of the model, wherein the machine learning model is a polynomial regression model.

[0076] The comprehensive stability score value of the convolutional neural network model is obtained by obtaining a corresponding function expression from comprehensive feature vector training data of the trained machine learning model: ; in the formula, is an output function of the model, MK is the descending ratio fluctuation index, MCBI is the average confidence bias index, is the comprehensive stability score value of the convolutional neural network model.

[0077] The obtained comprehensive stability score value of the convolutional neural network model is compared with a preset stability threshold value according to historical data, if the comprehensive stability score value of the convolutional neural network model is greater than or equal to the preset stability threshold value, it is indicated that the convolutional neural network model is stable in new mutation prediction, and no early warning signal is generated at this time; if the comprehensive stability score value of the convolutional neural network model is less than the preset stability threshold value, it is indicated that the convolutional neural network model is unstable in new mutation prediction, and an early warning signal is generated at this time.

[0078] The model optimization module is configured to update the architecture and the training data set of the convolutional neural network model based on the result of the comprehensive analysis module, so as to improve the prediction performance of the convolutional neural network model on new mutations.

[0079] According to the stability score and the stability feature vector [MK, MCBI], the architecture parameters Θ (such as the convolution kernel size, the number of layers, the number of channels, etc.) of the CNN model are optimized to minimize the instability of the model, and the target formula is: ; in the formula, Θ is the CNN model architecture parameter, MK(Θ) is the descending ratio fluctuation index under the current architecture, MCBI(Θ) is the average confidence bias index under the current architecture, and α and β are weight parameters, which measure the importance of MK and MCBI to the total target.

[0080] The architecture search algorithm (such as NAS, Neural Architecture Search) is used to dynamically optimize the model architecture: the convolution kernel size K, the number of layers L, and the number of channels C are searched: Automatically generate candidate architectures, evaluate their stability scores, and select the optimal architecture.

[0081] Introduce multi-scale convolution and regularization mechanisms to enhance adaptability to new mutations: multi-scale convolution The expression is: ; Different scale convolution kernels are represented. The regularization term: ; is the regularization term (such as Dropout, L2), which controls the model complexity and avoids overfitting, represents the total loss function, which is the optimization target of the model, represents the task loss of the model in specific tasks (such as classification, regression, mutation detection, etc.), and λ is the weight coefficient of the regularization strength.

[0082] Optimize the data distribution D to reduce the instability of the model by increasing the proportion of new mutation samples in the dataset or generating simulated new mutation samples, target formula: ; D is the current training dataset, MK(D) is the drop ratio fluctuation index under the dataset D, MCBI(D) is the average confidence bias index under the dataset D, γ, δ are weight parameters, is the optimized training dataset.

[0083] Increase the proportion of newly collected real new mutation samples : ; is the new mutation sequence collected from public databases (such as GISAID), is the original new mutation sample, is the real mutation sample; generate simulated new mutation samples using mutation patterns : ; According to the probability p, simulate insertion, deletion, substitution, etc.

[0084] Optimize the data distribution by adjusting the weight of new mutation samples in the training set: ; is the sample weight of class i, is the number of samples of class i, is the total number of samples in the training set, is the total number of classes.

[0085] Combine the dynamic updating strategy of architecture and dataset to optimize the comprehensive stability score of the model, optimization target: ; update the dataset D to minimize MK and MCBI: ; update the architecture , enhance the adaptability of the model to new mutations:​ wherein, is the current training dataset, is the updated training dataset, containing original data and new data. is the current model architecture parameter, including: convolution kernel size (such as 3x3, 5x5). The number of convolution layers (such as 5 layers, 10 layers). The number of channels (such as 32, 64, 128). ΔΘ is the architecture adjustment amount, including: adding or modifying the structure of the convolution layer (such as adding a multi-scale convolution layer). Change the regularization parameter (such as adjusting the Dropout ratio, introducing Batch Normalization). Adjust the model width or depth (such as increasing the number of channels or the number of network layers). is the updated architecture parameter, and the optimized model can better capture new mutation features.

[0086] In the embodiment, the prediction performance and stability of the convolutional neural network (CNN) for new mutations of viral genomes are improved through collaborative work. The data processing module is responsible for collecting and preprocessing viral genome sequences, and converting them into numerical values that can be processed by deep learning models through One-hot encoding and numerical mapping; the deep learning model module extracts local features from the encoded genome sequences based on the convolutional neural network, which is used for virus strain classification and mutation detection; the robustness evaluation module analyzes the fluctuation amplitude of the benchmark model performance decline ratio by constructing a new test dataset containing non-standard mutation forms, and evaluates the robustness of the model in new mutation prediction; the confidence analysis module collects the confidence distribution of the model prediction, analyzes the average confidence deviation of correct and incorrect prediction samples, and evaluates the abnormality degree of the confidence distribution; the comprehensive analysis module combines the robustness and confidence abnormality, generates a comprehensive stability score, and judges the stability of the model in new mutation prediction; the model optimization module dynamically updates the architecture and training dataset of the CNN model according to the comprehensive analysis results, continuously improves the prediction performance and adaptability of the model, and thus constructs an efficient and stable viral genome analysis and prediction system.

[0087] The above formulas are dimensionless numerical calculations, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the latest real situation. The preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0088] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center through wired (for example, infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0089] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A deep learning-based viral genomic data analysis and prediction system, characterized in that: The system comprises a data processing module, a deep learning model module, a robustness evaluation module, a confidence analysis module, a comprehensive analysis module, and a model optimization module. The data processing module is configured to collect, preprocess, and encode viral genome sequence data, including converting the genome sequence into a numerical form for processing by the deep learning model through One-hot encoding and numerical mapping. The deep learning model module comprises a convolutional neural network configured to extract local features from the encoded viral genome sequence and perform classification and mutation detection. The robustness evaluation module is configured to construct a new mutation test dataset containing non-standard mutation forms, and evaluate the robustness of the convolutional neural network model in predicting new mutations based on the fluctuation range of the decline ratio between the performance of the benchmark model and the performance of the new mutation dataset. The confidence analysis module is configured to collect the prediction confidence distribution of the convolutional neural network model on the new mutation test dataset, and analyze the average confidence deviation between correct and incorrect predictions to evaluate the degree of confidence distribution abnormality of the convolutional neural network model. Collecting the performance degradation ratio of all test samples from the evaluation experiment , forming a data set: ; wherein N is the total number of mutant samples, the mean of the performance degradation ratio is calculated , the calculation formula is: ; the variance of the performance degradation ratio is calculated , the calculation formula is: ; the degradation ratio fluctuation index is calculated, which is used to quantify the degree of performance fluctuation of the model on new mutant samples, and the expression is: ; wherein MK is the degradation ratio fluctuation index; The comprehensive analysis module is configured to combine the robustness of the convolutional neural network model in predicting new mutations and the degree of confidence distribution abnormality of the convolutional neural network model to generate a comprehensive stability score of the convolutional neural network model, and determine the stability of the convolutional neural network model in predicting new mutations. The model optimization module is configured to update the architecture and training dataset of the convolutional neural network model based on the results of the comprehensive analysis module to improve the prediction performance of the convolutional neural network model for new mutations. In the confidence analysis module, the average confidence deviation index is generated by analyzing the average confidence deviation between correct and incorrect predictions, and the average confidence deviation index is obtained by:

2. The deep learning-based viral genomic data analysis and prediction system of claim 1, wherein: In the comprehensive analysis module, the comprehensive stability score of the convolutional neural network model is generated by combining the robustness of the convolutional neural network model in predicting new mutations and the degree of confidence distribution abnormality of the convolutional neural network model, and the comprehensive stability score is calculated as follows: extracting a prediction confidence of each sample from a prediction result of the convolutional neural network model on the new mutation test dataset ; dividing the data into two groups: a correct prediction group: a set of confidences of samples for which the model predicted correctly ; Error prediction set: the set of confidence values for the samples for which the model prediction was wrong For each set of confidence data, compute its mean and standard deviation σ, and skewness, the expression for which is: ; S is the skewness value; the kurtosis is calculated, which measures the degree of sharpness of the confidence distribution, and the expression is: K is the kurtosis value, and based on the skewness and kurtosis values, the average confidence deviation index is defined, and the expression is: ; in the formula, is the average confidence deviation index.

3. The deep learning-based viral genomic data analysis and prediction system of claim 2, wherein: The decline ratio fluctuation index and the average confidence deviation index are converted into a comprehensive feature vector, the comprehensive feature vector is used as the input of a machine learning model, the machine learning model predicts the comprehensive stability score value label of the convolutional neural network model as the prediction target, the training target is to minimize the sum of prediction errors of all comprehensive stability score value labels of the convolutional neural network model, the machine learning model is trained until the sum of prediction errors converges, and the comprehensive stability score of the convolutional neural network model is determined based on the model output result, wherein the machine learning model is a polynomial regression model. ​ 4. The deep learning-based viral genomic data analysis and prediction system of claim 3, wherein: The comprehensive stability score value of the acquired convolutional neural network model is compared with a preset stability threshold value according to historical data. If the comprehensive stability score value of the convolutional neural network model is greater than or equal to the preset stability threshold value, it indicates that the convolutional neural network model is stable in new mutation prediction, and at this time, no early warning signal is generated. If the comprehensive stability score value of the convolutional neural network model is less than the preset stability threshold value, it indicates that the convolutional neural network model is unstable in new mutation prediction, and at this time, an early warning signal is generated.

5. The deep learning-based viral genomic data analysis and prediction system of claim 1, wherein: In the model optimization module, based on the result of the comprehensive analysis module, the architecture and the training data set of the convolutional neural network model are updated, specifically: Optimize the architecture parameters Θ of the CNN model to minimize the instability of the model, the target formula: ; In the formula, Θ is the architecture parameter of the CNN model, MK(Θ) is the drop rate fluctuation index under the current architecture, MCBI(Θ) is the average confidence bias index under the current architecture, and α and β are weight parameters; dynamically optimize the model architecture using an architecture search algorithm: search for the convolution kernel size K, the number of layers L, and the number of channels C: ​ Introducing multi-scale convolution and regularization mechanism to enhance the adaptability to new mutations: multi-scale convolution The expression is: ; Different scale convolution kernels are represented, and the regularization term is: ; The regularization term is to control the model complexity and avoid overfitting, The total loss function is represented by L, which is the optimization target of the model, The task loss of the model in the specific task is represented by L, and λ is the weight coefficient of the regularization strength.

6. The deep learning-based viral genomic data analysis and prediction system of claim 5, wherein: Enhancing the proportion of new mutation samples in the data set, target formula: ; D is the current training data set, MK(D) is the drop rate fluctuation index under the data set D, MCBI(D) is the average confidence bias index under the data set D, γ, δ are weight parameters, is the optimized training data set; increase the latest collected real new mutation samples : ; for new mutation sequences collected from public databases, for original new mutation samples, for real mutation samples; generate simulated new mutation samples using mutation patterns : ; on samples simulate mutation operations with probability p; By adjusting the weight of new mutation samples in the training set, the data distribution is optimized: ; is the weight of the sample of class i, is the number of samples of class i, is the total number of samples in the training set, is the total number of classes. In combination with the dynamic updating strategy of architecture and dataset, the comprehensive stability score of the model is optimized, and the optimization target is: ; updating dataset D, minimizing MK and MCBI: ; updating architecture , enhancing model adaptability to new mutations: ; where, is the current training dataset, is the updated training dataset containing original data and new data; is the current model architecture parameter, and ΔΘ is the architecture adjustment amount, is the updated architecture parameter.

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