36 results about "Virus identification" patented technology

The invention discloses an identification and analysis method for unknown pathogenic microorganisms, which comprises the following steps: filtering out genome sequences with low integrity, pollution and tag errors, and establishing a high-quality virus identification database; constructing a virus host prediction model through a machine learning algorithm; unknown pathogenic microorganisms are identified and analyzed, potential hosts or pathogenicity of the unknown microorganisms are identified, and whether the unknown microorganisms are unknown pathogenic viruses or bacteria or not is further judged. On the basis of metagenome data analysis, potential unknown pathogenic microorganisms in samples of human bodies, environments and the like can be identified more accurately.

The invention discloses a Teensy virus identification and protection method for a mobile hard disk, and relates to the technical field of virus identification and protection.The method effectively identifies high-simulation attack equipment through composite equipment fingerprint construction and combination of hardware ripple features and dynamic protocol response, and breaks through the limitation of traditional single feature detection; meanwhile, the protocol state transition probability model adopts hidden Markov chain real-time analysis, microsecond-level protocol switching abnormity is accurately captured, and the problem of missing detection caused by rough time granularity of an existing scheme is solved; in addition, a window context instruction association mechanism binds a system focus state with an HID operation, and blocks a hidden attack chain formed by legal instruction combination; the hierarchical fusing strategy fuses protocol endpoint control and physical layer isolation, so that the availability of a storage function is ensured, meanwhile, attack blocking is realized, and the situation that normal use is influenced due to full-port forbidding in a traditional scheme is avoided.

The invention discloses a different-length genome sequence coding method based on improved self-attention and Mamba double modules, and relates to a different-length genome sequence coding method. The invention aims to solve the problem that a genome sequence cannot be reasonably input into a neural network model in the prior art. The method comprises the following steps: in a data processing sequence feature extraction process, extracting kmer position feature information, kmer centrality feature information and inter-kmer relevance feature information of genome sequences with different lengths; in the pre-training process, a model based on improved self-attention and Mamba double modules is constructed, a sequence coding model can capture universal features of genome sequences through mask pre-training, and universal representation vectors of the same dimension of the genome sequences of different lengths are obtained; in the fine tuning process, by using specific biological task data for fine tuning training, the coding model can be better matched with various virus-based genomes such as virus recognition, virus host and virus lifestyle recognition. The invention belongs to the technical field of deep learning and gene engineering.

The application discloses a macrovirus group analysis method based on second-generation and third-generation sequencing technologies, which comprises the following steps: performing quality control and filtering on second-generation sequencing raw data to obtain clean reads, performing single-sample and multi-sample assembly on the quality-controlled data to obtain contigs sequences; performing correction and quality control on third-generation sequencing raw data to obtain clean long sequences, performing assembly on the quality-controlled third-generation data to obtain contigs sequences; performing mixed assembly on the quality-controlled data of the second-generation and third-generation sequencing to obtain contigs sequences; merging all contigs to construct a non-redundant contigs set; and finally performing virus identification and determination, virus species annotation and functional annotation. The application provides a reliable macrovirus group analysis method based on second-generation and third-generation sequencing technologies, and the implementation method is simple and the application range is wide.

The application belongs to the cross field of virology and bioinformatics, and particularly relates to a virus identification model construction method and system based on capsid proteins. The method collects capsid protein sequence data and non-capsid protein sequence data, and trains a capsid protein identification model; obtains capsid protein sequences with known structures, structure information and hierarchical classification information, and identifies a few-sample category with a sample quantity lower than a preset threshold; generates supplementary data of the few-sample category through a protein sequence design model and screens the supplementary data from the collected capsid protein sequence data, predicts the three-dimensional structure of the sequence in the supplementary data, and together with the corresponding classification information, forms a capsid protein supplementary data set for training a capsid protein classification model. The application alleviates the problems of data imbalance caused by insufficient data of the few-sample category and high classification difficulty of remote homologous proteins caused by single feature extraction, and is helpful to realize efficient preliminary identification of viruses.

The invention provides a physical AI virus identification method and system based on a Bayesian neural network, and belongs to the technical field of network security. According to the method, aiming at the fact that a target recognition object is a physical AI virus (including a physical confrontation sample, a physical backdoor, an unknown physical domain AI virus and the like), the physical confrontation sample is taken as an example, and the confrontation sample and a clean sample collected in a physical real world are creatively adopted to construct a training data set. According to the recognition framework provided by the invention, a multi-modal data fusion mechanism under simulation of real environment disturbance is utilized, higher robustness and generalization ability are shown in a complex physical scene, and AI viruses such as physical adversarial samples in the real world can be better recognized.

The application relates to the technical field of data processing, and discloses a multi-modal attention deep learning method for enhancing virus identification in metagenomic data, which comprises five main steps of data preprocessing, sequence embedding, multi-modal feature extraction, dynamic feature fusion and prediction, and hyperparameter optimization and training; through four model paths of GAT, a self-encoder, a convolutional long short-term memory network and a Transformer, the graph structure features, the latent features, the space-time features and the global features of sequences are respectively extracted, the self-attention mechanism is introduced, the contribution weights of the paths are adaptively adjusted according to the features of the input sequences, the sequence embedding is carried out by using the GAT and the self-encoder, the long-distance dependence relationship and the latent features are captured, the space-time and global features are extracted by using the ConvLSTM and the Transformer model paths, and the hyperparameter optimization and the training strategy optimization are carried out; the application improves the virus sequence identification capability, improves the feature fusion accuracy, and further improves the performance and the robustness of the model.

The application discloses a monoclonal antibody specifically binding to coxsackievirus A6 and application thereof. The monoclonal antibody comprises a light chain and a heavy chain, the amino acid sequence of the complementarity determining region HCDR1 of the heavy chain is shown as SEQ ID No:1, the amino acid sequence of the complementarity determining region HCDR2 of the heavy chain is shown as SEQ ID No:4, the amino acid sequence of the complementarity determining region HCDR3 of the heavy chain is shown as SEQ ID No:7, the amino acid sequence of the complementarity determining region LCDR1 of the light chain is shown as SEQ ID No:10, the amino acid sequence of the complementarity determining region LCDR2 of the light chain is shown as GTS, and the amino acid sequence of the complementarity determining region LCDR3 of the light chain is shown as SEQ ID No:13. The monoclonal antibody has good specificity and sensitivity and has a virus neutralization capacity, and shows great potential in virus identification, diagnosis, treatment and development of multivalent vaccines.

A method and system for virus detection in tumor RNA sequencing data are disclosed. The method includes preprocessing the raw tumor RNA sequencing data; inputting the processed tumor RNA sequencing data into sequence-information-based channels and codon-based channels for feature extraction to generate a feature matrix; constructing a sequence information prediction model and a codon information prediction model, and inputting the feature matrices generated from the sequence information-based channels and codon-based channels into the sequence information prediction model and codon information prediction model, respectively, for training and optimization; predicting the virus probability of each sequencing read to obtain a model score; and selecting viral sequencing reads based on the model scores to assemble viral contigs. This invention improves the accuracy and robustness of virus monitoring by introducing a multimodal deep learning method, and can adaptively process sequencing data from different sources and of different lengths, thereby better meeting the needs of medical and research fields for virus identification in tumor sequencing data.

The application discloses a macrovirus group data analysis method, and belongs to the technical field of macrovirology. The method comprises the following steps: sequence quality control, sequence assembly, sequence clustering, virus sequence identification, virus sequence checking, virus abundance calculation, species annotation, virus lifestyle judgment, virus host prediction and virus auxiliary metabolism gene analysis. The application uses Trimmomatic software, BWA-MEN, Megahit software, CD-HIT and other tools to execute the analysis process of macrovirus group data. Practice proves that the application can accurately identify and annotate virus species, comprehensively and systematically deeply analyze and mine macrovirus group data, the steps are simple and clear, the analysis time is short, and the effect of macrovirus identification research is greatly improved.

The application provides a novel 2019-nCoV nucleic acid detection kit, in particular, provides a kit and a method for multiplex detection of 2019-nCoV nucleic acid, which can simultaneously detect three nucleic acid targets of 2019-nCoV, can be verified by different targets, can prevent false positives, can confirm the missed detection caused by mutation, and significantly improves the accuracy of virus identification.

The embodiment of the present disclosure provides a computer network security server virtualization processing method and related equipment. The processing method includes: obtaining target input data through a data perception module and detecting the type of the target input data; the data perception module inputs the target input data into the target virtual server application layer according to the type of the target input data, so that the target virtual server application layer calls the target input data and generates dynamic operation data; filtering the dynamic operation data and the target input data through a data disguise module to obtain redundant disguise data, and storing the redundant disguise data in a file library; detecting abnormal files in the redundant disguise data in the file library through a data capture module, and storing the abnormal files in a separately divided virtualization system in the cloud virtual platform; and performing virus identification on the abnormal files through a data identification module. The technical solution disclosed in the present disclosure can isolate viruses from spreading in the virtual server application layer.

The invention discloses a micropterus salmoides rhabdovirus gene engineering vaccine preparation method, which comprises: S1, recombinant plasmid construction: carrying out PCR amplification on a micropterus salmoides rhabdovirus G protein gene with a 6 * His tag, cloning the amplified gene between BamH I and Hind III restriction enzyme cutting sites of a pVL1393 vector to obtain a pVL1393-G-His recombinant plasmid, S2, preparing a transfection system, and S3, carrying out purification on the transfection system to obtain the micropterus salmoides rhabdovirus gene engineering vaccine. S3, recombinant baculovirus preparation and identification, S31, cell transfection, S32, P1-generation virus harvesting, and S33, virus identification, the G protein of the micropterus salmoides rhabdovirus (MSRV) is directionally expressed through a genetic engineering technology, a large number of pathogenic viruses do not need to be cultured, the biological safety risk is greatly reduced, meanwhile, the defect that a prokaryotic expression system lacks protein post-translational modification is overcome, and the method is suitable for industrial production. The sf9 insect cell is used as an expression host, so that the recombinant G protein can be correctly folded to form a functional structure domain, and the immunogenicity is remarkably improved.

A real-time fluorescence RT-PCR detection method and a kit for a novel coronavirus 2019-nCoV. Specifically, the present invention relates to a kit and a method for detecting the nucleic acid of an E gene of the novel coronavirus 2019-nCoV. The kit and the method have extremely high sensitivity and specificity, and can significantly improve the accuracy of virus identification.

This invention discloses a primer design method and primer set. Through in-depth analysis of influenza surveillance network data nationwide, this application makes significant contributions to improving the accuracy and sensitivity of influenza virus identification. The primer revisions not only achieved significant results in experiments but also provided valuable insights for future influenza surveillance and research. A deeper understanding of the genetic characteristics and epidemiological patterns of influenza viruses is crucial for developing scientifically effective prevention and control strategies. In disease prevention and public health management, continuous improvement of surveillance methods and strategies will bring greater benefits to timely responses to infectious diseases such as influenza.

The invention provides an analysis method and system for virus metagenome sequencing data, and relates to the field of biological information.The method comprises the steps that quality filtering is conducted on original sequencing data, and filtered data is obtained; according to the filtered data, a sequence is assembled through a De Bruijn graph and an overlapping layout, and a virus metagenome sketch is constructed; comparing the virus metagenome sketch with a known virus database to identify known virus types; and identifying a new virus by combining the unknown sequence which is not compared with the virus sequence through clustering analysis so as to realize the sequence comparison of the virus metagene. By improving data quality and accuracy, enhancing virus identification and classification capability, improving variation detection and function annotation efficiency and optimizing gene prediction and community structure analysis, analysis of virus metagenome sequencing data is realized.

The application belongs to the technical field of biology. More particularly, it relates to a method for accurately identifying the genotype of African swine fever virus based on high-throughput sequencing. The application selects 29 SNP sites in the B646L (P72) gene region that are highly conserved and genotype-specific, calculates the proportion of specific sites by parallel alignment with double reference sequences and base depth quantification statistics, and realizes quantitative determination of the proportion of type I, type II and mixed infection of African swine fever virus. The application breaks through the limitation that the traditional method cannot determine the mixed infection of African swine fever virus, realizes the objective determination of mixed strains, and provides a reliable tool for virus identification under complex infection scenarios. The application is completed based on high-throughput sequencing data, the analysis process is standardized, the subjective error caused by manual judgment is reduced, the consistency and repeatability of the results are strong; at the same time, the output statistical table and the results are intuitive and traceable, and have good popularization and application value.

The application discloses a Teensy virus identification and protection method of a mobile hard disk, relates to the technical field of virus identification and protection, and breaks through the limitation of traditional single feature detection by combining hardware ripple characteristics with dynamic protocol response to effectively identify high-simulation attack equipment through composite equipment fingerprint construction. Meanwhile, a hidden Markov chain is used for real-time analysis of a protocol state transition probability model to accurately capture microsecond-level protocol switching abnormalities and solve the problem of missed detection caused by coarse time granularity of an existing scheme. In addition, a window context instruction association mechanism binds a system focus state to HID operation to block a hidden attack chain formed by a combination of legal instructions. A hierarchical fuse strategy integrates protocol endpoint control and physical layer isolation to realize attack blocking while ensuring the availability of storage functions, thereby avoiding the influence of normal use caused by full-port disabling in traditional schemes.

The application discloses an Internet Trojan virus detection method and device, the method comprises the following steps: configuring a Trojan virus environment, and collecting traffic data under the Trojan virus environment; dividing the collected traffic data according to five tuples in a bidirectional manner, and extracting features to obtain traffic feature data; obtaining training set data obtained by processing the traffic feature data, training a machine learning model by using the training set data, and obtaining a Trojan virus identification model; wherein the processing comprises: normalizing the traffic feature data; and identifying the hiding mode of the Trojan virus and the activity stage of the Trojan virus by using the Trojan virus identification model. The application realizes the detection of Trojan virus traffic, the locking of the hiding mode of the Trojan virus, and the identification of the activity stage of the Trojan virus under high concealment.

The invention discloses an attack virus detection method and apparatus, an electronic device and a medium, and belongs to the technical field of network security, for each node of a target cluster, collecting multi-dimensional behavior data of each container running on the node, and sliding a data feature type of target behavior data in a target window on the behavior data according to a preset step length, performing feature value conversion on the target behavior data, and combining the converted feature values to obtain a fusion feature vector; performing abnormal feature analysis on the fusion feature vector, and determining a first detection result of the target behavior data; if it is determined that the target behavior data is abnormal, feature reconstruction analysis is performed on the fusion feature vector, and a second detection result of the target behavior data is determined; determining a final result of the target behavior data based on the first detection result and the second detection result; and based on the final result of the target behavior data corresponding to each target window, determining whether the nodes in the target cluster have containers of attack viruses. And the attack virus identification precision is improved.

The embodiment of the invention relates to the technical field of virus recognition, in particular to a virus classification method, device and equipment based on hierarchical Transform and a computer readable storage medium, and the virus classification method based on hierarchical Transform comprises the following steps: obtaining a to-be-classified virus genome; extracting protein characteristics of a protein coding gene sequence in the virus genome as local input expression; extracting overall tissue information of the virus genome as global context representation; inputting the local input representation and the global context representation into the trained hierarchical Transform for feature processing and classification calculation to obtain a plurality of virus classification hierarchies and corresponding matching degrees; and determining a matched virus category label according to each virus classification level and the corresponding matching degree, and taking the matched virus category label as a virus classification result. According to the virus classification method based on the hierarchical Transform, multi-dimensional features are fused, dependence on a single reference can be reduced, and robustness and accuracy of virus classification are improved.

The application discloses a kit and a method for multiplex detection of novel coronavirus nucleic acid, which can simultaneously detect two nucleic acid targets of the novel coronavirus, has extremely high sensitivity and specificity, and significantly improves the accuracy of virus identification.

The application discloses a monoclonal antibody specifically binding to coxsackievirus A6 and application thereof. The monoclonal antibody comprises a light chain and a heavy chain, the amino acid sequence of the complementarity determining region HCDR1 of the heavy chain is shown as SEQ ID No: 2, the amino acid sequence of the complementarity determining region HCDR2 of the heavy chain is shown as SEQ ID No: 5, the amino acid sequence of the complementarity determining region HCDR3 of the heavy chain is shown as SEQ ID No: 8, the amino acid sequence of the complementarity determining region LCDR1 of the light chain is shown as SEQ ID No: 11, the amino acid sequence of the complementarity determining region LCDR2 of the light chain is AAT, and the amino acid sequence of the complementarity determining region LCDR3 of the light chain is shown as SEQ ID No: 14. The monoclonal antibody has good specificity and sensitivity and has a virus neutralization capacity, and shows great potential in virus identification, diagnosis, treatment and development of multivalent vaccines.

The embodiment of the invention provides a virus identification method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence, the method comprises the following steps: collecting operation data of to-be-monitored equipment in at least one time window, the operation data being used for representing an execution behavior generated by an operation behavior implemented on the to-be-monitored equipment; determining whether the operation data is normal behavior data or not through a first model; the first model is obtained through unsupervised learning of a sample representing normal operation data; if it is determined that the running data are abnormal behavior data, whether the running data are triggered by virus operation or not is determined through a second model; the second model is obtained by training a sample representing normal operation data and a sample representing abnormal operation data. The operation data is judged through unsupervised learning, and the problem that ransomware variation cannot be recognized is avoided. And whether the data is the ransomware is determined through the second model, so that the situation that the normal operation data is misjudged as the ransomware is avoided.

The invention belongs to the crossing field of virology and biological information technology, and particularly relates to a capsid protein-based virus recognition model construction method and system. The method comprises the following steps: collecting capsid protein sequence data and non-capsid protein sequence data, and training a capsid protein recognition model; acquiring a capsid protein sequence with a known structure, structure information and hierarchical classification information, and identifying a few-sample category of which the sample quantity is lower than a preset threshold value; the method comprises the following steps: generating a protein sequence design model, screening supplementary data of few sample categories from collected capsid protein sequence data, predicting a three-dimensional structure of a sequence in the supplementary data, and forming a capsid protein supplementary data set together with corresponding classification information for training a capsid protein classification model. According to the method, the problems of data imbalance caused by insufficient data of few sample categories, high difficulty in classification of distant homologous proteins caused by single feature extraction and the like are solved, and high-efficiency preliminary identification of viruses is facilitated.

The present application relates to the technical field of virus identification, in particular to a virus identification method in NGS sequence based on multi-dimensional feature fusion, comprising the following steps: S1, non-parametric assembly analysis and non-parametric feature extraction; S2, sample-specific reference sequence library construction; S3, parametric coverage analysis and feature extraction; S4, machine learning model design and pathogen accurate determination; S5, virus identification in NGS sequence. The present application performs non-parametric assembly analysis on NGS sequence, constructs a reference sequence for parametric comparison according to the results obtained by non-parametric assembly, carries out three kinds of parametric coverage analysis based on the constructed reference sequence library, finally designs a machine learning algorithm, constructs a training label based on qPCR verification data, screens sequence features and trains a model, uses the trained model as the final determination basis, and realizes accurate virus identification.

The application discloses a virus identification method and system based on instance segmentation, and the method comprises the following steps: acquiring basic characteristics of virus particles based on at least one preset morphologically similar virus image; dividing the virus image into area graphs, generating first feature maps corresponding to each area graph based on the area graphs; acquiring multi-scale virus characteristics of the virus particles based on the first feature maps and the basic characteristics, and generating second feature maps; decomposing the second feature maps into first attention feature maps, second attention feature maps and third attention feature maps; and performing fusion processing on the attention feature maps to generate an identification result. The method can obtain more accurate virus identification results and improve the virus identification accuracy.

This invention discloses a method for identifying and analyzing unknown pathogenic microorganisms, comprising the following steps: filtering out genomic sequences with low integrity, contamination, and incorrect labeling to establish a high-quality virus identification database; constructing a virus host prediction model using machine learning algorithms; and performing identification and analysis on the unknown pathogenic microorganisms to identify their potential hosts or pathogenicity, further determining whether they are unknown pathogenic viruses or bacteria. This invention, based on metagenomic data analysis, can more accurately identify potential unknown pathogenic microorganisms in human and environmental samples.

A method for identifying exogenous viruses based on metagenomic sequencing, comprising the following steps: S1, a data quality control step, filtering the quality of the metagenomic sequencing raw data of the sample to obtain clean sequencing data; S2, a host removal step, aligning the clean sequencing data with a host reference genome and a ribosomal RNA database, removing the host sequences on the alignment, and obtaining enriched microbial sequences. The method integrates kraken2 species identification, RVDB / nt_core database alignment, virsorter2 / checkV verification and other multiple links to form a multi-dimensional identification system, improve the accuracy and reliability of virus identification, introduce a multi-process parallel processing mechanism to support the synchronous analysis of batch samples, greatly improve the analysis efficiency, adapt to the high-throughput detection needs of large-scale cell banks, biological products intermediates and finished products, and solve the problem of low throughput of traditional methods and existing mNGS methods.

The application discloses a server disinfection method, device, equipment and medium, and relates to the technical field of computers.The server disinfection method creates a corresponding memory image for the memory system of a target operating system, and performs virus identification on the memory image; at this time, the target operating system can be in a closed state to avoid virus running, and thus the thoroughness of disinfection is ensured; in addition, the virus existing in the memory image is determined according to the virus dimension; at this time, virus identification can be performed without relying on a virus library, the limitation of virus identification is avoided, and thus the security of the computer system is ensured.