Virus genome data analysis and prediction system based on deep learning

Abstract

The invention discloses a viral genome data analysis and prediction system based on deep learning, and relates to the technical field of data management. Through a deep learning model module, a convolutional neural network is utilized to extract genome local features and realize classification and variation detection; the robustness evaluation module quantifies the performance fluctuation of the model in new mutation by analyzing a new mutation test data set; a confidence coefficient analysis module evaluates abnormity of confidence coefficient distribution predicted by the model, a comprehensive analysis module combines robustness and a confidence coefficient result to generate a comprehensive stability score, and the stability of the model to new mutation is dynamically quantified; and the model optimization module iteratively updates a model architecture and a training data set based on an analysis result, so that the prediction performance of the model on the dynamic variation of the virus is remarkably improved, the problem that the existing model is difficult to adapt to the accumulated drift of the virus genome mutation is effectively solved, an efficient and stable solution is provided for rapid detection of new mutation, and the method is suitable for popularization and application. Important public health significance is realized.

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 to distinguish differences between different virus strains and help predict the spread 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 the spike protein. Another application is to use generative adversarial networks (GAN) to predict the potential impact of viral mutations and provide 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: Virus variation is a dynamic, time-dependent process, and CNN models are usually trained based on static data sets, which 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 predictions of new mutations (especially functional mutations) can miss high-risk mutations, such as those that cause higher transmissibility, immune escape or drug resistance. Thus, it is not possible 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

[0005] 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.

[0006] 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. The data processing module is configured to collect, preprocess and encode the 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 includes 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 performance drop ratio between the benchmark model performance 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 prediction samples to evaluate the abnormality of the confidence distribution of the convolutional neural network model. The comprehensive analysis module is configured to combine the robustness of the convolutional neural network model in predicting new mutations and the abnormality 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. 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 on new mutations.

[0007] Preferably, in the robustness evaluation module, a performance drop ratio fluctuation index is generated after analyzing the fluctuation range of the performance drop ratio between the benchmark model performance and the performance of the new mutation dataset, and the performance drop ratio fluctuation index is obtained by: 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 , and the calculation formula is: ; the variance of the performance drop ratio is calculated as , and the calculation formula is: ; and the performance 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 performance drop ratio fluctuation index.

[0008] Preferably, in the confidence analysis module, an average confidence deviation index is generated after analyzing the average confidence deviation between correct and incorrect prediction samples, and the average confidence deviation index is obtained by: Extracting the prediction confidence of each sample from the prediction results of the convolutional neural network model on the new mutation test dataset ; divide the data into two groups: correct prediction group: the set of sample confidence that the model predicts correctly ; error prediction group: the set of sample confidence that the model predicts incorrectly ; 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 the 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: ; in the formula, is the average confidence deviation index.

[0009] Preferably, in the comprehensive analysis module, the robustness of the convolutional neural network model to new mutation prediction performance and the abnormal degree of the confidence distribution of the convolutional neural network model are combined to generate a comprehensive stability score of the convolutional neural network model, specifically: Convert the decline rate fluctuation index and the average confidence deviation index into a comprehensive feature vector, take the comprehensive feature vector as the input of the machine learning model, and take the comprehensive stability score value label of the convolutional neural network model predicted by each group of comprehensive feature vectors 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, and the model training is stopped when the sum of prediction errors converges. The comprehensive stability score value of the convolutional neural network model is determined according to the model output result, wherein the machine learning model is a polynomial regression model.

[0010] 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.

[0011] Preferably, in the model optimization module, based on the results of the comprehensive analysis module, the architecture and 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 is: In the formula, Θ represents the CNN model architecture parameters, MK(Θ) is the descent 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: searching for the kernel size K, number of layers L, and number of channels C. ; Introducing multi-scale convolution and regularization mechanisms to enhance adaptability to new mutations: Multi-scale convolution The expression is: ; Represents convolution kernels of different scales, and regularization terms: ; It is a regularization term that controls model complexity and avoids overfitting. The total loss function represents the objective of model optimization. This represents the task loss of the model in a specific task, and λ is the weight coefficient of the regularization strength.

[0012] Preferably, the proportion of new mutant samples in the dataset is increased, with the target formula being: D represents the current training dataset, MK(D) is the descent rate volatility index for dataset D, MCBI(D) is the mean confidence bias index for dataset D, and γ and δ are weight parameters. The optimized training dataset; Add the latest collected real new mutation samples : ; For new mutant sequences collected from public databases, For the original new mutant sample, Real mutation samples are used; simulated new mutation samples are generated using mutation patterns. : ; For the sample Simulate mutation operations with probability p; The data distribution is optimized by adjusting the weights of the new mutated samples in the training set. ; Let i be the sample weights. Let be the number of samples in class i. This represents the total number of samples in the training set. The total number of classes; By combining dynamic update strategies for architecture and dataset, the overall stability score of the model is optimized, with the optimization objective being: Update dataset D, minimize MK and MCBI: Update architecture To enhance the model's adaptability to new mutations: In the formula, the current training dataset, the updated training dataset, containing original data and new data; the current model architecture parameters, and the updated architecture parameters.

[0013] In the above technical solution, the present application provides technical effects and advantages: 1. The present application realizes efficient analysis of dynamic variation of viral genomes by combining data processing, deep learning modeling, robustness evaluation, confidence analysis, comprehensive analysis and model optimization six modules. Through dynamic updating of the 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.

[0014] 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 weights, 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

[0015] 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 described below are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art based on these drawings.

[0016] Figure 1 The system module diagram of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0018] Embodiment, please refer to Figure 1 As shown in the embodiment, the virus genomic data analysis and prediction system based on deep learning 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 used for collecting, preprocessing, and encoding the virus genomic sequence data, including converting the genomic sequence into a numerical form for deep learning model processing through One-hot encoding and numerical mapping. The deep learning model module comprises a convolutional neural network, which is used for extracting local features from the encoded virus genomic sequence and performing classification and mutation detection. The robustness evaluation module is used 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 amplitude of the decline ratio between the performance of the benchmark model and the performance of the new mutation dataset. The confidence analysis module is used for collecting the prediction confidence distribution of the convolutional neural network model on the new mutation test dataset, and analyzing 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. The comprehensive analysis module is used 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. The model optimization module is used 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 for new mutations.

[0019] In the data processing module, virus genomic sequence data is collected from public or private databases to ensure diversity and coverage of data sources. Virus databases such as GISAID and NCBI GenBank are connected through API or batch download tools to obtain sequence data. Typical formats are FASTA or CSV files, in which genomic sequences are represented as strings (such as "ATCG...GCTA"). Metadata collection includes virus strain name, collection time, geographic location, and host information to support subsequent analysis.

[0020] The raw genomic sequence data collected is cleaned and standardized to ensure data integrity and consistency. Check if there are missing fragments or incomplete sequences in the data, complete or remove incomplete samples. Remove redundant repeated sequences to avoid data bias. Align the sequences using multi-sequence alignment algorithms such as MAFFT, ClustalW, and ensure uniform reference coordinates for mutation sites. For the problem of inconsistent sequence length, perform truncation or padding operation to make the length uniform, such as fixed to 30,000 bases (typical SARS-CoV-2 full genome length).

[0021] In order to convert the viral genome sequence into a numerical form acceptable to the deep learning model, the data processing module provides the following two encoding methods: Each base (A, T, C, G) is represented by a unique four-dimensional vector to ensure 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 the 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.

[0022] Map each base 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 the sequence position: Example: for the sequence "ATCG", its encoding result is: [1,2,3,4]; Use the embedding layer (Embedding Layer) to map the numerical value to a continuous vector to improve feature expression ability.

[0023] The encoded viral genome sequence is output in the form of a standardized numerical tensor, which is the input of 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 value can be further embedded into a high-dimensional vector.

[0024] The deep learning model module includes a convolutional neural network for extracting local features from the encoded viral genome sequence and performing classification and mutation detection.

[0025] 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). A convolutional layer that extracts local features such as base patterns, mutation sites, and repeat sequences through convolution operations. A pooling layer that reduces the dimensionality of the feature map, preserving important features and improving computational efficiency. A fully connected layer that integrates the extracted local features for classifying virus strains or detecting mutations. An output layer that outputs classification probabilities or mutation detection results.

[0026] 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 convolution kernels (such as 3 or 5 in size) and further analyzed downstream.

[0027] 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 extra 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.

[0028] 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 tri-nucleotide or hexa-nucleotide repeats). Provide a comprehensive description of the number and length of repeat regions.

[0029] Genome boundary features: represent the boundaries between different functional regions of the genome (such as start and stop codons, non-coding region and coding region boundaries). Features include gene region boundaries usually have specific base patterns, such as conserved sequences in promoter regions. Use local convolution to extract these boundary features to identify the division of different regions of the genome.

[0030] Base frequency features: the frequency of occurrence of different bases (A, T, C, G) within a local window and their distribution patterns. Features include uneven base distribution may be related to functional hotspots (such as enhancers or splice sites). Multi-channel convolution kernels combined with local pooling operations can extract base frequency distribution patterns within a local window.

[0031] Long-range dependency features (combined with improved architecture): dependencies between distant positions in the genomic sequence (e.g., complementary sequences at the two ends or long-range interactions). Feature representation: commonly found in complementary sequences in RNA secondary structures. CNN extension: combined with multi-scale convolution kernels or combined with structures such as Transformers to capture long-range dependency features.

[0032] Convolution layer design includes: convolution kernel size: use different sizes of convolution kernels (such as 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 (such as 32, 64, 128) to extract more dimensional feature patterns.

[0033] 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 feature length after pooling. C is the number of feature channels, representing the number of types of local features extracted.

[0034] 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 benchmark model performance and the new mutation dataset performance.

[0035] 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: insertion, deletion or rearrangement of large fragments in the genome (e.g. gene structure abnormalities).

[0036] Evaluate the performance of the CNN model on the non-mutation test dataset (i.e. standard reference genome) and record the benchmark performance indicators. Performance indicators: classification task: accuracy, F1 score, ROC-AUC. Mutation detection task: sensitivity, specificity, mean absolute error (MAE).

[0037] Run the model on the new mutation test dataset, calculate the difference in performance indicators from the benchmark performance, and quantify the model robustness, formula: ; in the formula, is the performance decline ratio, is the benchmark performance, For new mutant dataset performance.

[0038] The drop rate fluctuation index is generated after analyzing the fluctuation range of the drop rate between the benchmark model performance and the new mutant dataset performance, evaluating the robustness of the convolutional neural network model for new mutant prediction performance. The method for obtaining the drop rate fluctuation index is: Collect the performance drop rate of all test samples from the evaluation experiment , forming a dataset: ; Where N is the total number of mutant samples, ensuring that all are valid values (no missing or abnormal values). The outliers exceeding ±3σ can be labeled or removed, and the mean value of the performance drop rate is calculated , the calculation formula is: ; Calculate the variance of the performance drop rate , the calculation formula is: ; Calculate the drop rate fluctuation index, which is used to quantify the degree of performance fluctuation of the model on new mutant samples, the expression is: ; In the formula, MK is the drop rate fluctuation index.

[0039] When the drop rate fluctuation index is larger, it means that the prediction performance of the convolutional neural network model on new mutants 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 may fail when faced with unseen mutant samples.

[0040] When the drop rate fluctuation index is smaller, it means that the prediction performance of the convolutional neural network model on new mutants is more stable, showing high robustness. This means that the model can maintain consistent performance on multiple mutation types and provide more reliable prediction results for new mutants. A low fluctuation index reflects the model's advantages in adaptability and generalization ability, providing more robust support for mutation detection and classification tasks, and performing well in handling diverse mutant data.

[0041] The confidence analysis module is used to collect the prediction confidence distribution of the convolutional neural network model on the new mutant test dataset, and analyze the average confidence deviation between correct prediction and error prediction samples, evaluating the abnormality degree of the confidence distribution of the convolutional neural network model.

[0042] The prediction confidence of a 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 in the Softmax output (Top-1 probability) is extracted as the prediction confidence. The confidence values of all test samples and their corresponding actual prediction results (correct or incorrect) are collected.

[0043] According to the prediction results of the test samples, the samples are divided into two groups: correct prediction group: samples predicted by the model consistent with the actual label. 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 of the confidence distribution.

[0044] After analyzing the average confidence deviation of correct and incorrect prediction samples, the average confidence deviation index is generated to evaluate the abnormality of the confidence distribution of the convolutional neural network model. The method for obtaining the average confidence deviation index is: 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).

[0045] The data is divided into two groups: correct prediction group: the confidence set of samples predicted correctly by the model ; Incorrect prediction group: the confidence set of samples predicted incorrectly by the model ; Ensure that the data has no missing values or outliers (such as confidence values exceeding the reasonable range, usually ∈[0,1].

[0046] For each group of confidence data, calculate its mean and standard deviation σ, as well as skewness, which is an important indicator of the symmetry of the confidence distribution, expressed as: ; S is the skewness value. S>0 is right-skewed, with confidence concentrated in higher values, and the model may be overly confident in some samples. S<0 is left-skewed, with confidence concentrated in lower values, and the model has low overall confidence. S=0 is symmetric, with uniform confidence distribution. 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 indicates that the distribution is steep and the confidence is concentrated on the extreme value (abnormally high or abnormally low), indicating that the model confidence distribution is abnormal. K<0 indicates that the distribution is flat and the confidence is relatively dispersed, and the model is less confident. K=0 is the characteristic of normal distribution, and the confidence distribution is normal. Based on the skewness and kurtosis values, the average confidence bias index is defined, and the expression is: ; wherein, is the average confidence bias index.

[0047] When the average confidence bias index (MCBI) is larger, it means that the abnormal degree of the confidence distribution of the convolutional neural network model is higher. This means that the confidence difference between correct samples and incorrect samples predicted by the model is significant, which may show excessive confidence (skewness value is large) for incorrect predictions or the confidence distribution is too concentrated on the extreme value (kurtosis value is large). 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.

[0048] When the average confidence bias index (MCBI) is smaller, it means that the abnormal degree of the confidence distribution of the convolutional neural network model is lower, and the confidence distribution of the prediction result is more reasonable. The confidence difference between correct prediction and incorrect prediction of the model is smaller, and the confidence distribution is more uniform without excessive concentration on the extreme value. This situation indicates that the prediction result of the model is more stable, and the confidence can better reflect the true ability of the model, thereby providing more reliable support for downstream decision-making.

[0049] The comprehensive analysis module is used to combine the robustness of the convolutional neural network model in predicting new mutations and the abnormal 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.

[0050] The decline ratio fluctuation index and the average confidence bias index are converted into a comprehensive feature vector, the comprehensive feature vector is taken as the input of a machine learning model, the machine learning model takes each set of comprehensive feature vector to predict the comprehensive stability score value label of the convolutional neural network model as the prediction target, minimizes the sum of prediction errors of the comprehensive stability score value labels of all convolutional neural network models as the training target, and trains the machine learning model until the sum of prediction errors converges to stop model training. The comprehensive stability score value of the convolutional neural network model is determined according to the model output result, wherein the machine learning model is a polynomial regression model.

[0051] The method for obtaining the comprehensive stability score value of the convolutional neural network model is: obtaining the corresponding function expression from the comprehensive feature vector training data of the trained machine learning model: ; wherein, is the output function of the model, MK is the drop ratio fluctuation index, and MCBI is the average confidence bias index. is the comprehensive stability score value of the convolutional neural network model.

[0052] The comprehensive stability score value of the convolutional neural network model obtained is compared with the stability threshold value preset 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 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 indicates that the convolutional neural network model is unstable in new mutation prediction, and an early warning signal is generated at this time.

[0053] The model optimization module is used to update the architecture of the convolutional neural network model and the training data set based on the results of the comprehensive analysis module, so as to improve the prediction performance of the convolutional neural network model for new mutations.

[0054] According to the stability score and the stability feature vector [MK, MCBI], the architecture parameters Θ (such as the size of the convolution kernel, 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 architecture parameter of the CNN model, MK(Θ) is the drop 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.

[0055] The architecture search algorithm (such as NAS, Neural Architecture Search) is used to dynamically optimize the model architecture: searching for the size of the convolution kernel K, the number of layers L, and the number of channels C: ; Automatically generating candidate architectures, evaluating their stability scores, and selecting the optimal structure.

[0056] Multi-scale convolution and regularization mechanism are introduced to enhance the adaptability to new mutations: multi-scale convolution The expression is: ; , which represents different scale convolution kernels. The regularization term is: ; , which is a regularization term (such as Dropout, L2), which controls the model complexity and avoids overfitting, , which represents the total loss function, is the target of model optimization, , which represents the task loss of the model in a specific task (such as classification, regression, mutation detection, etc.), and λ is the weight coefficient of the regularization strength.

[0057] 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, the target formula: ; D is the current training dataset, MK(D) is the drop rate 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.

[0058] 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 ; Sample Simulate insertion, deletion, substitution, etc. Mutation operation according to probability p.

[0059] Optimize the data distribution by adjusting the weight of the new mutation sample 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.

[0060] Combine the dynamic updating strategy of architecture and dataset to optimize the comprehensive stability score of the model, the optimization target: ; update the dataset D, minimize MK and MCBI: ; update the architecture , enhance the adaptability of the model to new mutations: ; in the formula, is the current training dataset, is the updated training dataset, which contains original data and new data. is the current model architecture parameter, including: convolution kernel size (such as 3×3, 5×5). The number of convolution layers (such as 5 layers, 10 layers). Channel number (such as 32, 64, 128). ΔΘ is the architecture adjustment amount, including: adding or modifying the structure of convolution layer (such as adding multi-scale convolution layer). Change the regularization parameter (such as adjust the Dropout proportion, introduce BatchNormalization). Adjust the model width or depth (such as increase the number of channels or network layers). is the updated architecture parameter, the optimized model can better capture the characteristics of new mutations. ​​

[0061] In the present embodiment, the prediction performance and stability of the convolutional neural network (CNN) for new mutations of viral genomes are improved by synergistic work. The data processing module is responsible for collecting and preprocessing viral genome sequences, and converting them into numerical form that can be processed by a deep learning model 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 viral 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.

[0062] 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.

[0063] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized wholly or partially 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 transferred from one computer readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center by wired (such as 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 and the like containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD) or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0064] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in 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 used 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. The deep learning model module comprises a convolutional neural network, which is used for extracting local features from the encoded viral genome sequence and performing classification and mutation detection. The robustness evaluation module is used 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 decline ratio between the performance of the benchmark model and the performance of the new mutation dataset. The confidence analysis module is used for collecting the prediction confidence distribution of the convolutional neural network model on the new mutation test dataset, and analyzing 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. The comprehensive analysis module is used 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. The model optimization module is used 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 for new mutations.

2. The deep learning-based viral genomic data analysis and prediction system of claim 1, wherein: In the robustness evaluation module, a decline ratio fluctuation index is generated after analyzing the fluctuation range of the decline ratio between the performance of the benchmark model and the performance of the new mutation dataset. Collect the performance degradation ratio of all test samples from the evaluation experiment , form a data set: ; where N is the total number of mutant samples, calculate the mean of the performance degradation ratio , the calculation formula is: ; calculate the variance of the performance degradation ratio , the calculation formula is: ; calculate the degradation ratio fluctuation index, which is used to quantify the degree of performance fluctuation of the model on new mutant samples, and the expression is: ; where MK is the degradation ratio fluctuation index.

3. The deep learning-based viral genomic data analysis and prediction system of claim 2, wherein: In the confidence analysis module, an average confidence deviation index is generated after analyzing the average confidence deviation of correct and incorrect prediction samples. 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.

4. The deep learning-based viral genomic data analysis and prediction system of claim 3, wherein: In the comprehensive analysis module, 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 are combined to generate a comprehensive stability score of the convolutional neural network model, which is specifically: The decline ratio fluctuation index and the average confidence deviation index are converted into a comprehensive feature vector, the comprehensive feature vector is taken as the input of a machine learning model, the machine learning model takes the predicted label of the comprehensive stability score of the convolutional neural network model as the prediction target, minimizes the sum of prediction errors of the predicted labels of all convolutional neural network models as the training target, trains the machine learning model until the sum of prediction errors converges, and then stops the model training, and finally determines the comprehensive stability score of the convolutional neural network model according to the output result of the model, wherein the machine learning model is a polynomial regression model.

5. The deep learning-based viral genomic data analysis and prediction system of claim 4, 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.

6. 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 a specific task is represented by L, and λ is the weight coefficient of the regularization strength.

7. The deep learning-based viral genomic data analysis and prediction system of claim 6, 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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