21 results about "Genetic Databases" patented technology

The invention relates to a digital intelligent regulation and control preparation method and system for a high-solid-waste low-carbon high-performance grouting material, and solves the problems that a traditional preparation technology lacks an autonomous and controllable digital intelligent regulation and control system and is weak in adaptability to fluctuation of raw materials, and the method comprises the following steps: obtaining XRF chemical components and XRD mineral composition data of industrial solid waste raw materials; constructing a material gene database; based on the database, screening a proportioning scheme by using a performance prediction model, and predicting workability, strength development and shrinkage performance; inputting a prediction result as a fitness function into a multi-objective optimization algorithm, and outputting an optimal material gene combination; a batching scheme is generated based on the optimal combination, and a stirring process is started after technological parameters are preset; collecting data through a real-time monitoring system, and comparing the data with the digital twin model; and based on a comparison result, automatically adjusting material proportioning parameters. The high-solid-waste, low-carbon and high-performance grouting material has the advantages that accurate design and regulation of the high-solid-waste, low-carbon and high-performance grouting material are achieved, material performance is improved, and carbon emission and cost are reduced.

The invention relates to the technical field of knowledge graph construction, in particular to a method for constructing a knowledge graph of autism multi-source heterogeneous big data. Original data is obtained from an electronic medical record system, a gene database, a research literature library and a social media multi-source heterogeneous data source in a real-time or timing mode; 2, cleaning, denoising, missing value completion and de-identification are carried out on the original data, and standardized mapping is carried out on terms based on a unified medical language system, the fifth edition of 'Mental Diagnostic and Statistical Handbook' and a human phenotype ontology medical ontology; 3, on the basis of the medical ontology, autism related concept subsets are expanded, version control is carried out, and a special autism ontology is formed. According to the method, high-precision fusion of multi-source entities is achieved, and the technical bottlenecks that standardization mapping is difficult, extraction precision is low, fusion alignment is difficult and the like are fundamentally solved.

The application discloses a disease transmission link prediction method and system based on a dynamic hypergraph neural network, aiming to comprehensively depict virus transmission paths and risk levels from multiple dimensions. First, data is collected from a gene database, mobile device logs and clinical texts, virus variation characteristics, population contact relationships and symptom semantic information are extracted respectively, and a dynamic hypergraph that integrates virus strain nodes, individual nodes and regional nodes is constructed. The hyperedge is defined according to molecular similarity, space-time contact and semantic overlap, and the weight is dynamically adjusted according to the transmission intensity of the infectious disease. A multi-modal hypergraph neural network with variation perception is introduced to jointly model and adaptively fuse multiple types of hyperedges and multi-scale features, output node propagation embedding vectors and predict potential transmission chains, perform prevention and control responses, and realize real-time deployment and feedback loop in an edge-cloud collaborative manner, ensuring efficient and accurate infectious disease transmission modeling and intervention control while ensuring data privacy.

The invention discloses a disease transmission link prediction method and system based on a dynamic hypergraph neural network, and aims to comprehensively describe a virus transmission path and a risk level from multiple dimensions. The method comprises the following steps: firstly, collecting data from a gene database, a mobile equipment log and a clinical text, respectively extracting virus variation characteristics, a crowd contact relationship and symptom semantic information, and constructing a dynamic hypergraph fusing virus strain nodes, individual nodes and region nodes; the hyperedge is defined according to molecular similarity, space-time contact and semantic overlap, and the weight is dynamically adjusted along with the infectious disease transmission intensity. A multi-modal hypergraph neural network of variation perception is introduced, joint modeling and adaptive fusion are performed on multi-type hyperedge and multi-scale features, a node propagation embedded vector is output, a potential propagation chain is predicted, prevention and control response is executed, and real-time deployment and feedback closed loop are realized in an edge-cloud cooperation mode. And efficient and accurate infectious disease transmission modeling and intervention control are completed on the premise of ensuring data privacy.

The application belongs to the technical field of gene detection, and discloses a method and system for judging the influence of homologous regions in NGS gene variation detection. The method comprises the following steps: obtaining a first type of gene and a plurality of second type of genes according to a public database; obtaining corresponding original sequence files of the first type of gene and the second type of gene based on a local gene database, obtaining corresponding sequence alignment files based on a local BAM database, and combining them to obtain known NGS gene data; calculating the GC content, the proportion of repeat units, and the similarity ratio in the original sequence file, and calculating the alignment quality in the corresponding sequence alignment file to construct a training sample; inputting the training sample into a classification model to obtain an optimized classification model; extracting the GC content, the proportion of repeat units, the similarity ratio, and the alignment quality from the NGS gene data to be detected and inputting them into the optimized classification model to judge whether the variation detection is affected. The application can judge the influence of homologous regions in variation detection without the aid of prior information of homologous regions.

The application discloses a biodiversity investigation and monitoring method based on satellite remote sensing and environmental DNA sequencing, comprising the following steps: S1, obtaining habitat remote sensing data and environmental DNA data; S2, preprocessing the obtained data and comparing with a public gene database to obtain species distribution data; S3, aligning and fusing the habitat remote sensing data and the environmental DNA data in time and space scales to generate a biodiversity feature data set; and S4, constructing a correlation model and a three-dimensional model between the habitat and the species distribution, and visually displaying the dynamic change of biodiversity. The application improves the comprehensiveness, accuracy and timeliness of investigation and monitoring, and provides a scientific basis for biodiversity protection.

The invention discloses a single cell annotation method and system based on gene sensitivity. The core process of the method comprises the following steps: firstly, extracting a related Marker gene list from a gene database to construct a reference data set; establishing an undirected network based on a protein interaction relationship of an effective Marker gene in a to-be-annotated cell, quantifying a gene comprehensive network weight by means of a network centrality index, performing probabilistic conversion on gene expression data, calculating gene divergence in combination with expression distribution, and determining a scarcity index according to the occurrence frequency of a reference data set. And then integrating the three to obtain a gene sensitivity index, introducing machine learning to fuse gene importance weight to eliminate divergence when annotation divergence of a cell and a clustering level exceeds a threshold value, and finally determining an annotation classification result according to comprehensive matching score of the cell to a known type.

The application discloses a kind of based on graph reinforcement learning's document-level event causal relationship identification method, comprising the following steps: obtaining text data containing event;Using large language model as external knowledge base and reasoner, generating prior causal graph, obtaining initial graph structure;Using long text pre-training language model to obtain the representation of contextualization event and calculate the correlation degree of event pair, provide prior knowledge;Introduce attention mask and self-attention initialization mechanism, generate the representation of contextualization event, integrate prior causal graph and contextualization event correlation into Transformer model to guide and control the training of model;Using graph reconstruction loss and attention distribution offset regularization loss to train and optimize model;Output the causal relationship between events extracted in gene database. The application introduces the self-attention initialization mechanism based on event correlation, and limits the attention of Transformer in reasonable distribution space.

The invention relates to the technical field of cable insulation thickness measurement, in particular to a cable insulation thickness measurement method and system based on an alternating curvature driving algorithm, and the method comprises the steps: collecting a cable cross section image, extracting an inner edge contour and an outer edge contour from the cable cross section image, obtaining an inner edge pixel coordinate set and an outer edge pixel coordinate set, respectively importing the inner edge pixel coordinate set and the outer edge pixel coordinate set into two pre-constructed blank arrays to obtain an inner edge pixel coordinate array and an outer edge pixel coordinate array, constructing an inner edge curvature gene database and an outer edge curvature gene database, generating an inner edge initial population and an outer edge initial population, and constructing an inner edge curvature gene database and an outer edge curvature gene database; and obtaining an optimal inner adaptation degree set and an optimal outer adaptation degree set, obtaining an optimal insulation thickness measurement coordinate set, and completing cable insulation thickness measurement based on the alternating curvature driving algorithm based on the optimal insulation thickness measurement coordinate set. According to the invention, the average detection time can be shortened and the minimum insulation thickness deviation can be improved.

The application provides a method for HLA typing and an electronic device thereof. The method comprises the following steps: S1) comparing the sequencing data of each sample gene with an HLA gene database to obtain a first candidate HLA gene typing set; S2) filtering the typing results with a coverage less than 30% or an average sequencing depth less than 30x in the first candidate HLA gene typing set to obtain a second candidate HLA gene typing set; S3) filtering the typing results with differences in the coding regions of the sample genes from the corresponding consensus sequences in the second candidate HLA gene typing set to obtain a final HLA typing result, wherein the sample HLA gene is obtained by sequencing with a third-generation sequencing platform.

This application provides a method, system, medium, product, and terminal for constructing a gene relationship knowledge graph. The method involves acquiring gene data from different databases and preprocessing the gene data; extracting entity information from the preprocessed gene data using a natural language model to generate an initial gene entity dataset and an initial gene entity relationship graph; performing data filtering on the initial gene entity dataset based on an adaptive filtering algorithm to obtain a target gene entity dataset; and constructing a gene relationship knowledge graph based on the target gene entity dataset, the initial gene entity relationship graph, and a pre-built gene database model. This not only comprehensively reveals the entirety of the gene network and its dynamic changes but also intuitively displays the interactions between genes through visualization, providing researchers with an intuitive and convenient research tool.

The present application relates to a screening system and method for plant directional breeding, comprising: a collection unit configured to acquire images of a plant to be tested, and a central controller comprising a gene database storing gene data associated with phenotype changes of the plant to be tested, the central controller being configured to generate phenotype data of the plant to be tested related to a development time node according to the images acquired by the collection unit, and divide the phenotype data into at least one data set based on a development stage of the plant to be tested, the central controller setting a keyword label of an associated phenotype change for the data set corresponding to the development stage, and generating a recommended gene set corresponding to the keyword label through the gene database, wherein the recommended gene set is a set of genes associated with changes in the development time node of the plant to be tested.

The invention discloses a method for detecting gelatin in sea cucumber based on a fluorescent PCR (Polymerase Chain Reaction) technology. The method comprises the following steps: determining a target and designing a primer and a probe; determining a target gene: selecting a mitochondrial gene aiming at the DNA which is easy to degrade after processing, and designing a primer and a probe for amplifying a short fragment; obtaining a specific sequence: obtaining target gene sequences of the target sea cucumber and common adulterated species from an NCBI gene database, and finding a unique specific sequence region of the sea cucumber through comparison; synthesizing a primer and a probe: designing and synthesizing the primer and the TaqMan probe on the basis of the specific region; preparing a reference substance; sample pretreatment and DNA extraction; preparing a fluorescent PCR reaction system and adding a sample; running a fluorescent PCR amplification program: placing the reaction tube in a real-time fluorescent quantitative PCR instrument; result analysis and interpretation: quality control inspection and sample result interpretation. According to the invention, a primer and a probe which are only complementarily combined with sea cucumber DNA are designed, and a fluorescence signal is generated in PCR amplification, so that specific detection can be realized.

The application relates to a digital intelligent control preparation method and system of high-solid-waste low-carbon high-performance grouting material, solves the problem that a traditional preparation technology lacks a self-controllable digital intelligent control system and has weak adaptation capability to material fluctuation, and the method comprises the following steps: obtaining XRF chemical component and XRD mineral composition data of industrial solid waste materials, and constructing a material gene database; based on the database, a performance prediction model is used to screen a proportioning scheme, working performance, strength development and shrinkage performance are predicted; the prediction result is input into a multi-objective optimization algorithm as a fitness function, and an optimal material gene combination is output; based on the optimal combination, a batching scheme is generated, a stirring process is started after presetting process parameters; data are collected through a real-time monitoring system and compared with a digital twin model; based on the comparison result, material proportioning parameters are automatically adjusted. The application has the following effects: precise design and control of high-solid-waste low-carbon high-performance grouting material are realized, material performance is improved, and carbon emission and cost are reduced.

The invention discloses a method for constructing an RNA interference vector of pathogenic genes of rice bakanae disease pathogenic bacteria, and relates to the technical field of genetic engineering.The construction method comprises the steps that pathogenic gene segments are obtained, specifically, the pathogenic gene sequences of the rice bakanae disease pathogenic bacteria are retrieved from a gene database, specific primers with specific restriction enzyme recognition sites are designed, and the specific primers are used for identifying the pathogenic genes of the rice bakanae disease pathogenic bacteria; the method comprises the following steps: carrying out PCR (Polymerase Chain Reaction) amplification by taking pathogenic bacteria genome DNA as a template, and recovering and purifying a target gene segment after agarose gel electrophoresis separation; by optimizing the construction process of the RNA interference vector, efficient silencing of a target gene is achieved, specific restriction enzyme recognition sites and specific primers are utilized, pathogenic gene segments of pathogenic bacteria of the rice bakanae disease are retrieved and amplified from a gene database, and the pathogenic gene segments of the pathogenic bacteria of the rice bakanae disease are obtained. And then the RNA is connected and transformed with an intermediate vector and a plant expression vector to finally obtain the RNA interference vector capable of silencing the pathogenic gene, and the method has higher construction efficiency and more accurate silencing effect.

Biomarkers for post-traumatic stress disorder (PTSD) and biomarkers for fibromyalgia (FM), which is associated with a high rate of PTSD, have been identified by narrowing down biomarker genes from PTSD-related gene database on the basis of biological events related to disease conditions. In addition, Chinese herbal medicines that are potential therapeutic agents for PTSD have been identified through the examination and comparison of gene induction by Chinese herbal medicines and antipsychotics in human keratinocytes. The present method can provide: biomarkers applicable as indicators for the diagnosis and the development of therapeutic agents for PTSD and FM; effective Chinese herbal medicines for PTSD and FM; and a screening method for therapeutic agents for PTSD and FM.

The invention relates to the technical field of biomedical treatment, and discloses a chronic inflammatory disease risk prediction method and system and a storage medium. The method comprises the following steps: acquiring and cleaning original sequence data related to chronic inflammatory diseases from a patient gene database, and constructing a structured variation interaction network; real-time physiological indexes and health status data are fused, and an enhanced variation feature set reflecting gene-environment interaction is extracted through feature propagation technologies such as a graph convolutional network; analyzing in combination with a historical health data time sequence record, and generating a time sequence enhanced variation feature sequence; generating a preliminary risk score sequence by dynamically evaluating the weight of the risk factor in the sequence; through probability distribution modeling, deviation correction and multi-source verification, a comprehensive risk prediction value for the chronic inflammatory disease is obtained; and generating a risk assessment report based on the predicted value. According to the invention, dynamic, accurate and personalized prediction of the chronic inflammatory disease risk is realized.

The invention discloses a sugarcane parent detection and positioning system based on big data, and relates to the field of agricultural biotechnology, the system comprises the following components: a data acquisition module, which adopts a spatio-temporal dynamic sampling strategy to acquire leaf samples for sequencing, and adjusts the sampling frequency of a sensor network according to growth characteristics, the method comprises the following steps: collecting gene expression change data, field growth data, soil component data and climate parameters of sugarcane parents in a seedling stage, a tillering stage and a mature stage; the method comprises the following steps: constructing a gene detection model through a gene detection module, collecting full-life-cycle gene expression data of various known sugarcane varieties to establish a gene database, comparing preprocessed gene data of target sugarcane parents in each stage with the gene data, and determining variety categories by calculating weighted sum of gene similarity in each stage; meanwhile, the genetic purity and genetic relationship are analyzed, the model is continuously optimized through a data verification optimization method, parameters are adjusted to ensure the detection effect, and the accuracy of parent variety recognition and genetic analysis is greatly improved.

The invention discloses a modern product innovative design method based on traditional process element fusion, which comprises the following steps: S1, establishing a dual-attribute material gene database of a traditional material, and establishing a physical deformation model of a modern carrier through finite element analysis; s2, a product design requirement is received, a material pattern collaborative algorithm is called to recommend a material combination and generate an adaptive pattern, and the pattern detail level is matched with the material precision; s3, simulating actual effects of the material and the pattern in AR, and collecting user evaluation and optimizing parameters of a material pattern collaborative algorithm through A / B test; therefore, the problems of neglect performance, dilution of pattern culture, insufficient durability, long design period, large subjective deviation, disjunction with user requirements and the like in the prior art are solved.

A gene alignment technology may be applied to a computer system including an optical computing chip. In a process of performing gene alignment, a first group of gene fragments may be first obtained from a gene database based on a to-be-tested gene sequence, where the first group of gene fragments includes a plurality of reference gene fragments that match some bases of the to-be-tested gene sequence. After the first group of gene fragments is obtained, the to-be-tested gene sequence and the plurality of reference gene fragments in the first group of gene fragments may be input into the optical computing chip to perform optical alignment.

The invention discloses a high-throughput RRAM (Resistive Random Access Memory) content addressable memory and a genome analysis search method, which realize higher storage density, lower storage overhead and higher parallel search throughput rate through collaborative design of circuit-level structure optimization and a search algorithm. The method can be widely applied to gene database retrieval, pattern recognition, edge intelligent calculation and other high-parallel pattern matching processing systems.