27 results about "Homology analysis" patented technology

The invention relates to the technical field of industrial robot control, and particularly discloses an intelligent foreign matter sorting robot control system based on AI recognition, which comprises a dynamic spatial feature extraction module, a manipulator motion state coding module, a collaborative conflict detection module, a dynamic trajectory optimization module and an execution control adjustment module, constructing a three-dimensional dynamic space model through multi-sensor fusion, and extracting spatial topological features by utilizing continuous coherence analysis; manipulator motion parameters are converted into topological space representation, and a track feature coding matrix is established; detecting interaction conflicts among the manipulators in real time by adopting a multi-scale coherence analysis method, and generating graded early warning signals; a collision avoidance track is optimized based on topological constraints and a virtual rejection field technology; precise execution is achieved through inverse kinematics of the Lie group theory and self-adaptive control.

The invention relates to the technical field of power equipment state monitoring, and discloses an AI-based power distribution switch arc fault prediction system, and the system carries out the persistent coherence analysis of dielectric response data collected by a sensing array through a topological feature extraction unit, and outputs a topological bar code representing an insulation degradation space structure. Further, a dynamic causal graph construction unit performs causal test on the topological characteristic time sequence to construct a dynamic causal graph for revealing the degradation evolution path; and finally, the fault evolution prediction unit adopts a space-time diagram neural network model to process the causal diagram sequence so as to realize accurate prediction of the fault probability and the degradation extension path. The system also integrates a closed-loop mechanism of adaptive excitation control and data verification correction. According to the method, topological data analysis and causal inference are deeply fused, the problems that a traditional method is low in precision and poor in interpretability are solved, and prediction accuracy, reliability and robustness are remarkably improved.

This invention discloses methanogenic lyases with the same protein family annotation as PeiR lyases and their applications. These enzymes are obtained through homology screening based on the known characteristics of PeiR lyases targeting peptide bonds in the methanogenic cell wall, using the Pfam protein family database annotation. PeiR lyase proteins participate in the biological process of hydrolyzing archaea cell walls, effectively killing methanogens and reducing methane production. Proteins homologous to this protein have potential methane-reducing potential. This invention integrates rumen microbial metagenomic sequencing data, uses a series of bioinformatics software to screen methanogenic viral proteins, and combines Pfam functional domain annotation for homology analysis, ultimately identifying a series of lyases. These lyases were successfully expressed in a prokaryotic expression system, and in vitro gas production experiments also showed that the crude enzyme solution significantly reduced methane production.

The invention discloses a topology enhancement prototype and geometric self-adaption few-sample point cloud semantic segmentation method, which belongs to the technical field of three-dimensional vision, and comprises the following steps of: extracting geometric features of point cloud from a support set and a query set by using a feature encoder; a topology guiding prototype optimization module is introduced, persistent coherence analysis is carried out on point clouds of each category in a support set, multi-scale topology features are extracted, topology descriptors are generated, and the topology descriptors are used for guiding structural adjustment of prototype representation; a difficult sample correction module is designed, a difficult sample area in a query set is identified based on a topology instability index, and fine correction is performed on geometric features in combination with local point density and curvature information; and constructing a topology perception optimization module. According to the method, topology priori is systematically introduced into a few-sample point cloud segmentation task for the first time, and a more stable and fine semantic segmentation result is realized under the condition of not depending on a large amount of annotations.

The invention discloses a multi-modal lung cancer risk dynamic assessment system and method, and belongs to the technical field of medical data analys.The multi-modal lung cancer risk dynamic assessment system comprises a data acquisition module, a topological representation module, a self-adaptive attention gating module, a risk assessment module and a feedback optimization module; the method comprises the following steps: constructing a family history topological graph, a gene mutation topological graph and an environmental exposure topological graph, extracting multi-level topological features, generating a continuous bar code and a Betti number sequence based on continuous homology analysis, calculating an interaction coefficient, and triggering a cross-modal risk collaborative amplification mechanism when a gene mutation and environmental toxin coexistence mode is detected. The system is further provided with a three-level collaborative amplification mechanism, a risk decomposition report containing gene risks, environment risks, family risks and collaborative amplification factors is generated, dynamic parameter optimization is achieved by calculating the topological entropy change rate, deep fusion of multi-modal data is achieved, and the risk factor interaction effect is accurately quantified.

This invention provides a method for mining tumor antigens derived from endogenous retroviruses. The method involves bulk RNA-seq sequencing of tumor tissue and adjacent normal tissue samples using a splicing strategy to obtain DNA sequences of human endogenous retrovirus (HERV)-derived transcripts (hervRNA). Subsequently, a specific screening strategy is employed to obtain candidate open reading frames (ORF) sequences on tumor-specific hervRNAs. Finally, homology analysis, prediction of binding energy to human leukocyte antigen class I (HLA-I) molecules, and multi-site analysis are used to predict and mine neoantigens derived from HERV in tumors.

This invention discloses a method for constructing a Brca2-p.Q462Ter genetically engineered mouse and its application. The Brca2 genetically engineered mouse provided by this invention carries a truncated mutation in its Brca2 gene. This mutation is located at exon 10 of the mouse Brca2-201 transcript, changing amino acid position 462 from glutamine Q (Gln) to the stop codon Ter, forming the Brca2-p.Q462Ter mutation. The Brca2-p.Q462Ter genetically engineered mouse is used to simulate the human BRCA2 gene truncated mutation p.Gln472Ter, c.1414C>T. Amino acid homology analysis shows that the mouse BRCA2 protein Q462 site is homologous to the human BRCA2 protein Q472 site; therefore, this mouse mutation site can be used to simulate the corresponding truncated mutation effect in human BRCA2. This genetically engineered mouse can simulate, at the whole animal level, the abnormal DNA damage repair, impaired homologous recombination repair function, decreased genome stability, and susceptibility to related diseases caused by BRCA2 truncated mutations, thus providing a reliable tool for research on BRCA2-related pathogenesis, developmental effects, drug screening, and efficacy evaluation.

The invention discloses a high-order topological brain network analysis method and system based on a high-density electroencephalogram, and belongs to the field of brain network analys.The method comprises the steps that original data of the high-density electroencephalogram are obtained, preprocessing and source positioning are conducted, and a current density time sequence of a brain region of interest is obtained; k-order co-fluctuation is calculated, and multi-brain-region collaborative interaction is quantized; a high-order function connection matrix is constructed based on co-fluctuation, and threshold screening and sparsification are carried out; performing function module identification on the high-order function connection matrix, and extracting brain network topology features, including graph theory, weighted simple complex construction, weight assignment under a symbol remapping rule, illegal triangle detection, persistent coherence analysis, and super-consistency and super-complexity calculation; and carrying out machine learning classification and output. The invention provides a high-order brain network analysis framework under high time resolution, is suitable for the fields of auxiliary diagnosis of neuropsychiatric diseases, cognitive evaluation, brain-computer interfaces and the like, and realizes non-invasive brain function evaluation.

The application discloses a kind of gene data analysis systems based on AI, through multi-omics data matrix;Through 1D-CNN one-dimensional convolutional neural network, local correlation pattern mining is carried out to the multi-omics data matrix, the topological structure of gene network is identified by persistent homology analysis, and dynamic weight is allocated to sequence feature and topological feature matrix using dynamic attention mechanism, and the weighted multi-scale feature vector is output;Based on the architecture of Transformer, a multi-modal fusion model is established to fuse the multi-scale feature vector, and the general genomic features of the pre-training model are fine-tuned to adapt to the disease data set, and the fusion feature vector is output;The fusion feature vector is input into MLP multilayer perceptron for disease risk prediction, and a disease risk prediction index is generated by combining SHAP algorithm, and an auxiliary decision scheme is generated according to the prediction index. The accuracy and generalization ability of disease risk classification are effectively improved.

The present application relates to a kind of biological species homology analysis systems based on protein / gene sequence data, belong to biological species homology analysis technical field, solve the problem of high cost and low efficiency of detection method in prior art.The system of the present application includes: data preprocessing module, for the obtained protein / gene original data is carried out data cleaning, obtain protein / gene sequence data;Data analysis module includes: protein sequence analysis unit, for according to the frequency of each amino acid in protein sequence, position and its physical and chemical properties, generate protein sequence numerical representation vector;Gene sequence analysis unit, for obtaining the optimal matching sequence in two groups of gene sequences;Species homology judging module, for according to protein sequence numerical representation vector / optimal matching sequence, the distance of each two groups of protein / gene sequence data is calculated, and according to the size of distance, the analysis of protein / gene sequence homology nearness.

The invention discloses an engineering cost anomaly identification and early warning method based on machine learning. The method comprises the following steps: collecting engineering data, and processing the engineering data to generate a state vector sequence; constructing a state diagram, and generating a topological complex; performing continuous homology analysis on the topological complex, calculating topological characteristics, and generating a target engineering topological fingerprint sequence; generating a historical topological fingerprint sequence, performing vectorization embedding, and determining a normal behavior baseline region; carrying out vectorization embedding on the topological fingerprint sequence of the target project, and comparing with a normal behavior baseline region to generate a topological anomaly score; and executing power bifurcation detection processing, and generating a cost early warning signal when a bifurcation sign occurs or the topology anomaly score exceeds an identification threshold. According to the method, topology coherence analysis and power bifurcation identification are fused, accurate early warning of engineering cost evolution abnormity is realized, and the method has the advantages of early identification, high stability and strong applicability.

The application discloses an AhFMO gene for regulating peanut seed size and kernel weight and application thereof, relates to the technical field of bioengineering, and has the technical scheme as follows: 1. An AhFMO gene for regulating peanut seed size and kernel weight and application thereof, wherein the gene sequence is SEQ ID 1; 2. A method for screening candidate genes, wherein the method is realized by combining whole genome association analysis (GWAS) and genetic map positioning linkage analysis (QTL mapping) joint analysis, combining transcriptome analysis gene differential expression, homology analysis and overexpression verification; and 3. Application of the AhFMO gene for regulating peanut seed size and kernel weight, wherein the gene can regulate the size and kernel weight of seeds.

The invention relates to the field of bioinformatics, in particular to a susceptibility gene visualization decision support system and a method thereof.According to the system, a multi-level heterogeneous network of family and gene variation is constructed, topological feature extraction and Laplacian spectrum analysis are combined, a high-risk variation transmission path is recognized, and a high-risk variable transmission path is obtained; multi-scale persistent homology analysis is utilized to reveal risk structure hierarchies, dynamic display of a family-gene association map is realized through interactive visualization, finally, a risk hierarchy tree and a clinical knowledge base are integrated, accurate clinical decision support is provided for users, a complete analysis process from data to decision is realized, and the risk analysis efficiency is improved. A multi-level heterogeneous network is constructed based on a topology invariant theory, and integration expression of family relationship and gene variation data in a unified topology space is realized for the first time, so that doctors can intuitively understand distribution and association modes of variation in families.

The present application relates to a kind of biological species homology analysis methods based on protein sequence data, solve the problem of high cost and low efficiency of detection method.The method of the present application comprises: obtaining the protein sequence of X group biological species, according to the occurrence frequency of various amino acids in each group of protein sequence and the physicochemical properties of each amino acid, generate the amino acid frequency information vector and the average value vector of amino acid physicochemical properties of X group protein sequence;According to the position information of 1~K letter amino acid in protein sequence, generate X group amino acid position probability vector;Dimension reduction is carried out to each group of amino acid position probability vector, and the dimension-reduced amino acid position probability vector is obtained;According to amino acid frequency information vector, amino acid physicochemical properties average value vector and dimension-reduced amino acid position probability vector, the distance of each two groups of protein sequence is calculated, and the distance is analyzed according to the size of protein sequence homology.

The application provides an attack homologous analysis method, system, electronic equipment and storage medium, and relates to the technical field of network security. The method comprises the following steps: obtaining event clues, alarm data and sample data of a to-be-tested attack event; extracting ATT&CK tactics and technology features from the alarm data, extracting sample HASH values and code features from the sample data, and extracting attack industry and country features from the event clues; respectively calculating the similarity between the extracted features and corresponding features in a preset APT organization feature library; calculating a comprehensive similarity score based on each similarity, and determining the homologous APT organization of the to-be-tested attack event according to the comprehensive similarity score. The application integrates multiple-dimensional data sources, breaks through the analysis blind area caused by a single data source, simultaneously fuses static code and dynamic alarm features, effectively overcomes the false alarm problem caused by incomplete confusion code disassembly, and significantly improves the accuracy, comprehensiveness and robustness of attack tracing and homologous identification.

This invention discloses an event aggregation and attribution method based on attack chain homology analysis, comprising the following steps: Step 1, multi-source heterogeneous data fusion and standardization processing; Step 2, dynamic subject-object relationship modeling; Step 3, attack event homology analysis; Step 4, homology attack event aggregation; Step 5, attack path deduction and visualization; Step 6, dynamic threat adaptive optimization. This invention improves the completeness of attack path reconstruction and solves the defect of static modeling in failing to capture attack chain evolution by using a dynamic subject-object relationship model and homology analysis algorithm; it presents the attack process through visualization technology, shortening the analysis time and improving the efficiency of source tracing; it improves the robustness of homology determination and the accuracy of entity relationship identification by using a multi-feature weighted fusion hierarchical clustering mechanism combined with code string features and vulnerability exploitation methods for auxiliary verification; and it enhances the adaptability of dynamic threats and shortens the response window for new threats through a dynamic weight update mechanism.

The application discloses a granular material liquefaction prediction method based on a persistent homology method. First, according to the mutual contact relation between the particles in the granular system, the original granular system is discretized into a contact force network; then, based on the persistent homology method, the contact force threshold value of each homology invariant in the contact force network is identified through multi-scale homology analysis, the corresponding persistence diagram is constructed, the extraction of key homology structure information and the dimension reduction of the contact force network are realized; subsequently, the distance between two adjacent time persistence diagrams is calculated, and the evolution rate of the contact force network with the shearing process is quantified; further, whether the contact force network evolution rate curve has a mutation point is detected, and if the mutation point exists, it indicates that the contact force network has started to collapse at a high speed, which indicates the occurrence of liquefaction. The application provides a new and effective way for quantitatively characterizing the dynamic behavior of the contact force network of the granular material and establishing the macro-micro cross-scale research of the granular material.

The invention discloses bacteriocin AM12LCIr as well as a preparation method and application of the bacteriocin AM12LCIr. The invention provides a bacteriocin AM12LCIr, the coding sequence of the bacteriocin AM12LCIr is composed of 46 amino acids, the relative molecular weight is 5067.73 Da, homology analysis shows that the bacteriocin AM12LCIr has the highest homology with class II bacteriocin LCI bacteriocin, but the AM12LCIr and LCI have obvious differences in structure and physicochemical properties, and the bacteriocin AM12LCIr belongs to a novel bacteriocin. According to the present invention, the bacteriocin AM12LCIr is subjected to heterologous expression, and the prepared recombinant protein has good inhibition effect on a variety of bacteria, can inhibit Escherichia coli, Listeria monocytogenes, Enterobacter aerogenes, Salmonella paratyphi B, Staphylococcus aureus and Bacillus cereus, has broad-spectrum antibacterial activity, can be used for preparing more antibacterial products, and has broad application prospects. The invention has important significance for clinically preventing and treating a plurality of pathogenic bacteria including escherichia coli.

This invention discloses a method and system for parsing radiological image data, belonging to the field of data analysis technology. The method includes: registering and extracting features from PET metabolic images and ultrasound images to generate physiological metabolic feature vectors; reconstructing the organ surface manifold based on the real parts of compressed composite features, and combining Gaussian curvature detection and vascular continuous homology analysis to obtain manifold feature vectors; constructing a dynamic hypergraph based on the physiological metabolic feature vectors and manifold feature vectors to generate cross-modal fusion feature vectors; inputting the cross-modal fusion feature vectors into a constraint generator to solve the elastic wave equation and generate a diagnostic report; combining the elastic wave equation with a Monte Carlo random perturbation model, quantifying deformation anomalies through finite element nodal displacement fields, and combining morphological-metabolic-topological multi-dimensional parameters to significantly improve the sensitivity of malignant lesion detection.

The application discloses a kind of based on persistent homology's 3D box girder point cloud data topological structure identification method, comprising: S1, the point cloud of box girder is obtained, the three-dimensional dataset is generated by pre-processing point cloud;S2, according to three-dimensional dataset, persistent homology analysis is carried out, and the topological feature of box girder is obtained;S3, the topological feature of box girder is analyzed, and the region where there is structural problem is marked;S4, according to the region marked, topological feature is interpreted, and health assessment report is generated.By persistent homology technology, combined with the topological analysis of point cloud data, noise robustness, geometric mechanics interpretation and multi-scale feature capture, it aims to realize the multi-scale, robust and highly explanatory topological structure analysis of box girder point cloud data, the invention can capture the complex topological features of box girder at different scales, overcome the shortcomings of traditional geometric analysis method, especially in dealing with noise and uneven point cloud data, with higher robustness and reliability.

The invention relates to an open source component homologous analysis method and device, equipment and a medium. The method comprises the steps of obtaining standardized component parameters of a to-be-tested target, analyzing a source code of the to-be-tested target to obtain a code attribute graph, and performing graph-level representation learning on the code attribute graph to obtain to-be-tested whole graph embedding; querying the standardized component parameters in a preset component knowledge graph, and retrieving to-be-tested whole graph embedding in a structure semantic feature library to generate a candidate set; calculating a structural similarity score and an atlas consistency score of each candidate item in the candidate set, and performing fusion rearrangement on the structural similarity score and the atlas consistency score according to a preset weighting strategy to generate a final candidate set; judging the final candidate set based on a preset rule reasoning strategy to generate a corresponding judgment result; the judgment result is written into the component knowledge graph for graph updating, and a structured interpretable report is generated according to the updating result. The method has the effect of improving the homologous analysis accuracy of the open source component.

The present application belongs to the technical field of bio-information analysis, and provides a method for identifying lineage-specific expanded gene families. The present application is a method for screening and identifying lineage-specific expanded genes in different subfamilies by using orthology analysis. The method is simple to operate, requires a small amount of data, and the analysis software is easy to learn to operate. The screened genes are well representative, which lays a foundation for future large-scale identification and functional analysis of lineage-specific genes.

The application provides a transcription factor analysis method and device, electronic equipment and a storage medium. The transcription factor analysis method comprises the following steps: obtaining protein sequences of a species to be analyzed and a plurality of target transcription factors of the protein sequences; determining a target motif corresponding to each target transcription factor by performing homology analysis on the plurality of target transcription factors and pre-stored template transcription factors; performing co-expression analysis on each target transcription factor and the corresponding target motif to obtain a co-expression module of each target transcription factor; screening the co-expression module based on the target motif to obtain a regulatory subunit corresponding to each target transcription factor; and obtaining a transcription factor analysis result of the species to be analyzed by calculating an activity score of the regulatory subunit. The application can improve the efficiency of transcription factor analysis.

The invention discloses an event aggregation and attribution method based on attack chain homologous analysis. The event aggregation and attribution method comprises the following steps: step 1, performing multi-source heterogeneous data fusion standardization processing; 2, modeling a dynamic subject-object relationship; step 3, attack event homology analysis; step 4, homologous attack event aggregation; step 5, deducing and visualizing an attack path; step 6, dynamic threat adaptive optimization; through the dynamic subject-object relation model and the homology analysis algorithm, the attack path restoration integrity is improved, and the defect that attack chain evolution cannot be captured by static modeling is overcome; the attack process is presented through a visualization technology, the research and judgment time consumption is shortened, and the traceability efficiency is improved; the robustness of homology judgment and the accuracy of entity relationship recognition are improved by combining a hierarchical clustering mechanism of multi-feature weighted fusion with auxiliary verification of code character string features and a vulnerability utilization mode; and through a dynamic weight updating mechanism, the dynamic threat adaptability is enhanced, and a novel threat response window is shortened.

The invention relates to the technical field of biology, and particularly discloses a method for multi-species gene family identification and analysis, which comprises the following steps: S1, data preparation, S2, sequence extraction and comparison library construction, S3, bidirectional BLAST screening, S4, conserved domain and homology verification, S5, member naming and physicochemical property prediction, and S6, phylogenetic and collinearity analysis. According to the method, the recognition accuracy of gene family members is effectively improved by combining bidirectional BLAST screening with structural domain verification and homology analysis, and the method is particularly suitable for gene families with conservative structures and complex functions, such as C2H2 type zinc finger protein. The obtained member set has excellent performance in the aspects of structural domain integrity and phylogenetic consistency, false positive interference is remarkably reduced, and scientificity and representativeness of family mining are ensured; according to the invention, the C2H2 gene family is systematically identified and analyzed in various notopterygium plants for the first time, and the conservative and specific changes of the family in species evolution are disclosed.

Embodiments of the present application provide a method and device for homologous analysis of an Internet Protocol address, and a storage medium. The method comprises: obtaining an Internet Protocol address to be analyzed and corresponding original features; obtaining a plurality of reference Internet Protocol addresses and reference features corresponding to each reference Internet Protocol address; establishing a heterogeneous information graph structure based on associated sub-features between the original features and each reference feature; inputting the heterogeneous information graph structure into a target model; updating the original features of a target node based on the reference features associated with each reference node, the associated sub-features between the reference features and the original features associated with the target node, and the associated weights of the connection edges between each reference node and the target node, to obtain target features; and outputting a homologous analysis result corresponding to the Internet Protocol address to be analyzed based on the target features by the target model. In this way, the accuracy of homologous analysis of the Internet Protocol address can be improved.