Method and system for managing entomophilous pathogen genome data

Abstract

The invention discloses an entomophilous pathogen genome data management method and system. The entomophilous pathogen genome data management method comprises the following steps: collecting genome data related to entomophilous pathogens; combining the genome data related to the entomophilous pathogens to generate a data set; carrying out analysis and standardization processing on the data set; inputting the standardized data set into an anomaly detection model, and identifying an abnormal value or noise data; the identified abnormal value is corrected; according to the method, the collected data are combined by combining the Hash algorithm, the neural network and the clustering algorithm, so that the data consistency of different data sources is ensured, and the designed anomaly detection model can more accurately identify and correct the abnormal value and noise in the entomophilous pathogen genome data; and the data quality and the accuracy of an analysis result are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of genome data analysis, and in particular to a method and system for managing genome data of insect-borne pathogens. Background Art

[0002] In bioinformatics research, the importance of insect-borne pathogen genomic data is becoming increasingly prominent. However, these data often come from multiple different data sources (such as NCBI, GISAID, etc.), with various formats and uneven data quality. When storing, analyzing and applying these data, the consistency, accuracy and availability of the data must be addressed.

[0003] Existing data processing methods mostly rely on manual or semi-automated cleaning and standardization methods, which are inefficient and prone to human errors. In addition, as new data are constantly generated, how to ensure the dynamic update and real-time nature of the data is also an urgent problem to be solved. Therefore, it is particularly important to design an algorithm that can automatically and intelligently process multi-source genomic data and perform standardization and quality control on the data. Summary of the invention

[0004] In order to overcome the defects in the above-mentioned prior art, the present invention provides a method for managing genomic data of insect-borne pathogens, including: collecting genomic data related to insect-borne pathogens; merging the genomic data related to the insect-borne pathogens to generate a data set; parsing and standardizing the data set; inputting the standardized data set into an anomaly detection model to identify outliers or noise data; and correcting the identified outliers.

[0005] As a preferred solution of the method for managing genomic data of insect-borne pathogens described in the present invention, the genomic data related to the insect-borne pathogens includes complete genomic sequences, partial gene sequences, annotation data, variation data and environmental data.

[0006] As a preferred solution of the method for managing genomic data of insect-borne pathogens described in the present invention, the merging of genomic data related to the insect-borne pathogens includes: generating multiple single-dimensional hash values ​​according to the dimensions of the genomic data related to the insect-borne pathogens, merging the multiple single-dimensional hash values ​​and performing hash operations to generate a composite hash value; using a pre-trained genome sequence model to extract features of the genomic data related to the insect-borne pathogens to obtain feature vectors; using cosine similarity to calculate the similarity between feature vectors; classifying the feature vectors according to the similarity and through a clustering algorithm; merging the feature vectors according to the classification results, adding source labels to the merged feature vectors, and generating a data set.

[0007] As a preferred solution of the method for managing genomic data of insect-borne pathogens described in the present invention, it includes: constructing a training set based on historical insect-borne pathogen genomic data, the training set including normal data and known abnormal data; quantum state encoding the training set; inputting the encoded quantum state into an anomaly detection model, and training the anomaly detection model through a variational quantum algorithm, adjusting the model parameters of the anomaly detection model to minimize the classification error; the anomaly detection model includes a feature mapping layer, a data processing layer and a classification layer, the feature mapping layer is used to encode the input data into a quantum state; the data processing layer is used to process the quantum state to capture the relationship between features and perform classification tasks; the classification layer is used to measure the quantum state to determine the category of the data point.

[0008] As a preferred solution of the method for managing genome data of insect-borne pathogens described in the present invention, the correction of the identified outliers includes gene sequence correction and variation correction.

[0009] As a preferred solution of the insect-borne pathogen genome data management system described in the present invention, it includes: a data capture module, configured to collect genomic data related to insect-borne pathogens; a data merging module, configured to merge the genomic data related to the insect-borne pathogens to generate a data set; a data processing module, configured to parse and standardize the data set; an anomaly detection module, configured to input the standardized data set into an anomaly detection model to identify outliers or noise data; a correction module, configured to correct the identified outliers.

[0010] As a preferred solution of the insect-borne pathogen genome data management system described in the present invention, the genome data related to the insect-borne pathogen includes a complete genome sequence, a partial gene sequence, annotation data, variation data and environmental data.

[0011] As a preferred solution of the insect-borne pathogen genome data management system described in the present invention, the data merging module is specifically configured to execute: generating multiple single-dimensional hash values ​​according to the dimensions of the genomic data related to the insect-borne pathogen, merging the multiple single-dimensional hash values ​​and performing hash operations to generate a composite hash value; using a pre-trained genome sequence model to extract features of the genomic data related to the insect-borne pathogen to obtain feature vectors; using cosine similarity to calculate the similarity between feature vectors; classifying the feature vectors according to the similarity and through a clustering algorithm; merging the feature vectors according to the classification results, adding source labels to the merged feature vectors, and generating a data set.

[0012] As a preferred solution of the insect-borne pathogen genome data management system described in the present invention, the anomaly detection module is specifically configured to execute: constructing a training set based on historical insect-borne pathogen genome data, the training set includes normal data and known abnormal data; quantum state encoding the training set; inputting the encoded quantum state into the anomaly detection model, and training the anomaly detection model through a variational quantum algorithm, adjusting the model parameters of the anomaly detection model to minimize the classification error; the anomaly detection model includes a feature mapping layer, a data processing layer and a classification layer, the feature mapping layer is used to encode the input data into the quantum state; the data processing layer is used to process the quantum state to capture the relationship between features and perform classification tasks; the classification layer is used to measure the quantum state to determine the category of the data point.

[0013] As a preferred solution of the insect-borne pathogen genome data management system described in the present invention, the correction of the identified outliers includes gene sequence correction and variation correction.

[0014] Beneficial effects of the present invention: The present invention combines the collected data by combining hash algorithm, neural network and clustering algorithm, thereby ensuring the data consistency of different data sources, and the designed anomaly detection model can more accurately identify and correct outliers and noise in the genome data of insect-borne pathogens, significantly improving the data quality and the accuracy of analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0016] Figure 1 This is a flow chart of the method for managing insect-borne pathogen genome data according to the first embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0020] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0021] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0022] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0023] Example 1

[0024] Reference Figure 1 , which is the first embodiment of the present invention, provides a method for managing genome data of insect-borne pathogens, comprising:

[0025] S1: Collect genomic data related to insect-borne pathogens.

[0026] The genomic data related to the insect-borne pathogens include complete genome sequences, partial gene sequences, annotation data, variation data and environmental data.

[0027] Complete genome sequence: Complete genome sequence data of insect-borne pathogens, usually stored in FASTA or GenBank formats. These sequence data contain all genetic information of the pathogen and are the basic data for analysis and research;

[0028] Partial gene sequence: In some cases, researchers may only provide or need sequence information of specific genes, for example, data on the coat protein gene or specific antigen gene of the virus;

[0029] Annotation data: including gene function annotation, sequence alignment results, gene expression information, etc. Usually, these data are stored in formats such as GFF and BED;

[0030] Variant data: including SNPs (single nucleotide polymorphisms), Indels (insertions and deletions) and other variant data, usually stored in VCF (Variant Call Format) format, used to analyze genetic variation between different samples;

[0031] Environmental data: Environmental metadata related to pathogens, such as sample collection location, time, host information, etc., are usually stored in CSV or JSON files.

[0032] S2: Merging the genomic data related to the insect-borne pathogen to generate a data set.

[0033] (1) Generate multiple single-dimensional hash values ​​according to the dimensions of the genomic data related to the insect-borne pathogen, merge the multiple single-dimensional hash values ​​and perform hash operation to generate a composite hash value; for example, sequence hash: use the SHA-256 algorithm to hash the gene sequence ATCGAGC and calculate the hash value H1; annotation hash: hash the annotation content Gene1 and calculate the hash value H2; function hash: hash the function description Enzyme to obtain the hash value H3; variant hash: hash the variant information Variant1 to obtain the hash value H4.

[0034] Combine the above hash values ​​and use a connector (such as "|") to generate a composite hash signature in the format of H_combined = H1 | H2 | H3 | H4; perform SHA-256 hashing on the combined string again to generate the final composite hash value H_final.

[0035] Preferably, the hash signature serves as a unique identifier for the data item, greatly reducing the existence of redundant data. By introducing multi-dimensional hashing, the present application is able to capture multiple features of the data item, so that data items with the same sequence but different annotations or variants have different hash signatures, thereby improving the uniqueness of the data identifier.

[0036] (2) Use the pre-trained genome sequence model (Transformer-based model) to extract features from genomic data related to insect-borne pathogens and obtain feature vectors;

[0037] For example, use BERT to embed the sequence ATCGAGC and obtain the vector representation V1.

[0038] (3) Use cosine similarity to calculate the similarity between feature vectors;

[0039] For example, the similarity is calculated for the vector representations of the sequences ATCGAGC and ATCGACC, and the similarity is calculated for the vector representations of the sequences ATCGAGC and GTCGAGC.

[0040] (4) classifying the feature vectors according to the similarity and using a clustering algorithm;

[0041] Use the DBSCAN algorithm to classify the feature vectors, parameter settings: set epsilon to 0.2 (distance threshold), min_samples to 2 (minimum number of samples);

[0042] The cosine similarity between vectors V1 (sequence ATCGAGC) and V2 (sequence ATCGACC) is calculated to be 0.95.

[0043] Clustering results: Data items with high similarity are grouped into the same group, ATCGAGC and ATCGACC are grouped into one group, and GTCGAGC is grouped into another group.

[0044] Preferably, by combining the genome sequence model and the clustering algorithm, similar data items can be identified more accurately.

[0045] (5) Merge the feature vectors according to the classification results, add source labels to the merged feature vectors, and generate a data set.

[0046] Similar or repeated data items divided by the clustering algorithm are merged, and source labels are attached to the merged feature vectors. After the merger, each feature vector will be accompanied by one or more data source labels to identify the data sources from which the feature vector comes. Even if the feature vectors are merged, the data source labels will be retained to ensure the traceability of the data.

[0047] S3: Parse and standardize the dataset.

[0048] Parse the genomic information in the data set, including gene sequence, annotation information, variation information, and environmental information. During the parsing process, the data structure and content will be checked to ensure the integrity and consistency of the data format. If the data format is inconsistent or a field is missing, the data item will be marked for subsequent processing.

[0049] Furthermore, the data of the dataset are converted into consistent formats and units and normalized, such as sequence length, GC content, etc.

[0050] S4: Input the standardized dataset into the anomaly detection model to identify outliers or noise data.

[0051] A training set is constructed based on historical insect-borne pathogen genomic data. The training set includes normal data and known abnormal data to ensure that the model can learn the differences between normal and abnormal data to improve the generalization ability of the model.

[0052] The training set is quantum-state encoded to encode the training set into the state of quantum bits (for example, by quantum phase encoding or quantum amplitude encoding).

[0053] Inputting the encoded quantum state into an anomaly detection model, and training the anomaly detection model through a variational quantum algorithm (variational quantum eigensolver), adjusting model parameters of the anomaly detection model to minimize classification error;

[0054] The anomaly detection model includes a feature mapping layer, a data processing layer and a classification layer. The feature mapping layer is used to encode the input data into a quantum state; the data processing layer is used to process the quantum state to capture the relationship between features and perform classification tasks. The data processing layer includes quantum logic gates (such as CNOT gates and RZ gates); the classification layer is used to measure the quantum state to determine the category of the data point (such as the probability of occurrence of |0> or |1>).

[0055] Furthermore, the standardized data set is input into the anomaly detection model to identify outliers or noise data, where outliers include abnormal gene sequences, abnormal GC content, abnormal variant data, etc.

[0056] Abnormal gene sequences: If the length of a gene sequence is abnormally short or long, it may indicate an error in the data collection or storage process. For example, a normal gene sequence may be between a few thousand and tens of thousands of base pairs, while an abnormal value may be far above or below this range.

[0057] Abnormal GC content: If the GC content in a gene sequence deviates significantly from the normal range (e.g., the GC content of a normal organism is between 40% and 60%), it may be marked as abnormal.

[0058] Abnormal variant data: If the variant data (such as SNPs) appear abnormally high or low in the sample, it will be considered abnormal data.

[0059] Noisy data include missing data, inconsistent data, low-quality sequences, etc.

[0060] Missing data: Data items with a high missing rate in the data field (such as a large amount of annotation information of a gene is missing) will be identified as noise data.

[0061] Inconsistent data: If the annotation information of the same gene in different data sources is inconsistent, it is marked as noise data.

[0062] Low-quality sequences: Sequences containing a large number of uncertain bases (N) or sequences with low alignment scores will be marked as noise data.

[0063] S5: Correct the identified outliers.

[0064] Correction of identified outliers includes gene sequence correction and variation correction.

[0065] Gene sequence correction: For abnormal gene sequences, such as sequences that are too short or contain a large number of N bases, the system will try to find similar sequences in the known sequence database and automatically fill in the missing parts or replace the low-quality parts;

[0066] Variant correction: For abnormal items in variant data, the system will make corrections based on the overall variation frequency of the sample population, or directly mark them as suspicious data for subsequent manual review.

[0067] Example 2

[0068] This embodiment is different from the first embodiment in that it provides an insect-borne pathogen genome data management system, including:

[0069] The data capture module is configured to collect genomic data related to insect-borne pathogens; wherein the genomic data related to insect-borne pathogens include complete genome sequences, partial gene sequences, annotation data, variant data and environmental data. Complete genome sequence: The complete genome sequence data of insect-borne pathogens are usually stored in FASTA and GenBank formats. These sequence data contain all the genetic information of the pathogens and are the basic data for analysis and research; Partial gene sequence: In some cases, researchers may only provide or need sequence information of specific genes, for example, data for the capsid protein gene or specific antigen gene of the virus; Annotation data: including gene function annotations, sequence alignment results, gene expression information, etc., usually, these data are stored in GFF, BED and other formats; Variant data: including SNPs (single nucleotide polymorphisms), Indels (insertions and deletions) and other variant data, usually stored in VCF (Variant Call Format) format, used to analyze genetic variations between different samples; Environmental data: Environmental metadata related to pathogens, such as sample collection location, time, host information, etc., usually stored in CSV or JSON files.

[0070] A data merging module is configured to merge the genomic data related to the insect-borne pathogen to generate a data set;

[0071] The data processing module is configured to perform parsing and standardization of the data set; parse the genomic information in the data set, including gene sequence, annotation information, variation information, and environmental information. During the parsing process, the data structure and content will be checked to ensure the integrity and consistency of the data format. If the data format is inconsistent or the field is missing, the data item will be marked for subsequent processing. Furthermore, the data in the data set is converted into a consistent format and unit for normalization, such as sequence length, GC content, etc.

[0072] An anomaly detection module is configured to input the standardized data set into an anomaly detection model to identify outliers or noise data;

[0073] The correction module is configured to perform correction on the identified outliers. The correction of the identified outliers includes gene sequence correction and variation correction. Gene sequence correction: For abnormal gene sequences, such as sequences that are too short or contain a large number of N bases, the system will try to find similar sequences in a known sequence database, automatically fill in the missing parts or replace the low-quality parts; variation correction: For abnormal items in the variant data, the system will make corrections based on the overall variation frequency of the sample population, or directly mark them as suspicious data for subsequent manual review.

[0074] Among them, the data merging module is specifically configured to execute: generate multiple single-dimensional hash values ​​according to the dimensions of the genomic data related to the insect-borne pathogen, merge the multiple single-dimensional hash values ​​and perform hash operations to generate a composite hash value; use a pre-trained genome sequence model to extract features of the genomic data related to the insect-borne pathogen to obtain a feature vector; use cosine similarity to calculate the similarity between feature vectors; classify the feature vectors according to the similarity and through a clustering algorithm; merge the feature vectors according to the classification results, add source labels to the merged feature vectors, and generate a data set.

[0075] Among them, the anomaly detection module is specifically configured to perform: construct a training set based on historical insect-borne pathogen genome data, the training set includes normal data and known abnormal data, and ensures that the model can learn the difference between normal and abnormal data to improve the generalization ability of the model. The training set is quantum state encoded, and the training set is encoded into the state of quantum bits (for example, through quantum phase encoding or quantum amplitude encoding). The encoded quantum state is input into the anomaly detection model, and the anomaly detection model is trained by a variational quantum algorithm (variational quantum eigensolver), and the model parameters of the anomaly detection model are adjusted to minimize the classification error; the anomaly detection model includes a feature mapping layer, a data processing layer and a classification layer, the feature mapping layer is used to encode the input data into the quantum state; the data processing layer is used to process the quantum state to capture the relationship between features and perform classification tasks, and the data processing layer includes quantum logic gates (such as CNOT gates, RZ gates); the classification layer is used to measure the quantum state to determine the category of the data point (such as the probability of occurrence of |0> or |1>). Furthermore, the standardized data set is input into the anomaly detection model to identify outliers or noise data, where outliers include abnormal gene sequences, abnormal GC content, abnormal variant data, etc.

[0076] Abnormal gene sequences: If the length of a gene sequence is abnormally short or long, it may indicate an error in the data collection or storage process. For example, a normal gene sequence may be between a few thousand and tens of thousands of base pairs, while an abnormal value may be far above or below this range.

[0077] Abnormal GC content: If the GC content in a gene sequence deviates significantly from the normal range (e.g., the GC content of a normal organism is between 40% and 60%), it may be marked as abnormal.

[0078] Abnormal variant data: If the variant data (such as SNPs) appear abnormally high or low in the sample, it will be considered abnormal data.

[0079] Noisy data include missing data, inconsistent data, low-quality sequences, etc.

[0080] Missing data: Data items with a high missing rate in the data field (such as a large amount of annotation information of a gene is missing) will be identified as noise data.

[0081] Inconsistent data: If the annotation information of the same gene in different data sources is inconsistent, it is marked as noise data.

[0082] Low-quality sequences: Sequences containing a large number of uncertain bases (N) or sequences with low alignment scores will be marked as noise data.

[0083] It should be appreciated that embodiments of the present invention may be implemented or enforced by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The method may be implemented in a computer program using standard programming techniques - including a non-transitory computer-readable storage medium configured with a computer program, wherein the storage medium so configured causes the computer to operate in a specific and predefined manner - according to the methods and drawings described in the specific embodiments. Each program may be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, if desired, the program may be implemented in an assembly or machine language. In any case, the language may be a compiled or interpreted language. In addition, the program may be run on a programmed ASIC for this purpose.

[0084] Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed collectively on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions that may be executed by one or more processors.

[0085] Further, the method can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Aspects of the present invention can be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, a RAM, a ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the process described herein. In addition, the machine-readable code, or part thereof, can be transmitted via a wired or wireless network. When such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor, the invention described herein includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques described in the present invention, the present invention also includes the computer itself. The computer program can be applied to input data to perform the functions described herein, thereby converting the input data to generate output data stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents a physical and tangible object, including a specific visual depiction of the physical and tangible object produced on a display.

[0086] As used in this application, the terms "component", "module", "system", etc. are intended to refer to a computer-related entity, which can be hardware, firmware, a combination of hardware and software, software, or software in operation. For example, a component can be, but is not limited to: a process running on a processor, a processor, an object, an executable file, a thread in execution, a program, and / or a computer. As an example, an application running on a computing device and the computing device can both be components. One or more components can exist in a process and / or thread in execution, and a component can be located in a computer and / or distributed between two or more computers. In addition, these components can be executed from various computer-readable media having various data structures thereon. These components can communicate in a local and / or remote process manner, such as according to a signal having one or more data packets (e.g., data from a component that interacts with another component in a local system, a distributed system, and / or interacts with other systems in a signal manner through a network such as the Internet).

[0087] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for managing genomic data of insect-borne pathogens, characterized in that: include: Collect genomic data related to insect-borne pathogens; Merging the genomic data related to the insect-borne pathogen to generate a data set; Parsing and standardizing the data set; Input the standardized data set into the anomaly detection model to identify outliers or noisy data; Correct the identified outliers.

2. The method for managing insect-borne pathogen genome data according to claim 1, characterized in that: The genomic data related to the insect-borne pathogens include complete genome sequences, partial gene sequences, annotation data, variation data and environmental data.

3. The method for managing insect-borne pathogen genome data according to claim 2, characterized in that: Merging the genomic data related to the insect-borne pathogen includes: Generate multiple single-dimensional hash values ​​according to the dimensions of the genomic data related to the insect-borne pathogen, merge the multiple single-dimensional hash values ​​and perform hash operation to generate a composite hash value; Use the pre-trained genome sequence model to extract features from genomic data related to insect-borne pathogens and obtain feature vectors; Use cosine similarity to calculate the similarity between feature vectors; Classifying the feature vectors according to the similarity and by a clustering algorithm; The feature vectors are merged according to the classification results, and source labels are added to the merged feature vectors to generate a data set.

4. The method for managing insect-borne pathogen genome data according to claim 3, characterized in that: include: Construct a training set based on historical insect-borne pathogen genome data, which includes normal data and known abnormal data; Performing quantum state encoding on the training set; Inputting the encoded quantum state into an anomaly detection model, training the anomaly detection model through a variational quantum algorithm, and adjusting model parameters of the anomaly detection model to minimize classification errors; The anomaly detection model includes a feature mapping layer, a data processing layer and a classification layer. The feature mapping layer is used to encode the input data into a quantum state; the data processing layer is used to process the quantum state to capture the relationship between features and perform classification tasks; and the classification layer is used to measure the quantum state to determine the category of the data point.

5. The method for managing insect-borne pathogen genome data according to claim 4, characterized in that: The correction of the identified outliers includes gene sequence correction and variation correction.

6. A genomic data management system for insect-borne pathogens, characterized in that: include: A data capture module, configured to collect genomic data related to insect-borne pathogens; A data merging module is configured to merge the genomic data related to the insect-borne pathogen to generate a data set; A data processing module is configured to perform parsing and standardization processing on the data set; An anomaly detection module is configured to input the standardized data set into an anomaly detection model to identify outliers or noise data; The correction module is configured to perform correction on the identified abnormal values.

7. The insect-borne pathogen genome data management system according to claim 6, characterized in that: The genomic data related to the insect-borne pathogens include complete genome sequences, partial gene sequences, annotation data, variation data and environmental data.

8. The insect-borne pathogen genome data management system according to claim 7, characterized in that: The data merge module is specifically configured to perform: Generate multiple single-dimensional hash values ​​according to the dimensions of the genomic data related to the insect-borne pathogen, merge the multiple single-dimensional hash values ​​and perform hash operation to generate a composite hash value; Use the pre-trained genome sequence model to extract features from genomic data related to insect-borne pathogens and obtain feature vectors; Use cosine similarity to calculate the similarity between feature vectors; Classifying the feature vectors according to the similarity and by a clustering algorithm; The feature vectors are merged according to the classification results, and source labels are added to the merged feature vectors to generate a data set.

9. The insect-borne pathogen genome data management system according to claim 8, characterized in that: The anomaly detection module is specifically configured to perform: Construct a training set based on historical insect-borne pathogen genome data, which includes normal data and known abnormal data; Performing quantum state encoding on the training set; Inputting the encoded quantum state into an anomaly detection model, training the anomaly detection model through a variational quantum algorithm, and adjusting model parameters of the anomaly detection model to minimize classification errors; The anomaly detection model includes a feature mapping layer, a data processing layer and a classification layer. The feature mapping layer is used to encode the input data into a quantum state; the data processing layer is used to process the quantum state to capture the relationship between features and perform classification tasks; and the classification layer is used to measure the quantum state to determine the category of the data point.

10. The insect-borne pathogen genome data management system according to claim 9, characterized in that: The correction of the identified outliers includes gene sequence correction and variation correction.

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