170 results about "Genetic analysis" patented technology

A federated distributed computational system enables secure biological data analysis and genomic medicine with enhanced oncological therapy capabilities. The system implements patient-specific tumor-on-a-chip analysis through microfluidic control systems and cellular heterogeneity preservation, while integrating fluorescence-enhanced diagnostics using CRISPR-LNP targeting and robotic surgical navigation. The architecture coordinates spatiotemporal analysis of gene therapy delivery through molecular imaging and immune response tracking, and implements bridge RNA integration with multi-target synchronization. Treatment selection is optimized through multi-criteria scoring and patient-specific simulation modeling. Each federated node contains a local processing unit for biological data analysis, privacy preservation protocols, and a hierarchical knowledge graph structure. The system implements cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration, enabling research institutions to collaborate on complex, large-scale biological analyses while maintaining strict data privacy controls.

A federated distributed computational system enables secure, multi-institutional biological data analysis and genomic medicine through interconnected, decentralized nodes in a federated distributed graph architecture. A federation manager coordinates computational resource allocation, control and data flows, establishes privacy and security boundaries, implements multi-scale spatiotemporal analysis and simulation modeling, models cross-species or intrapopulation elements, and maintains cross-institutional knowledge relationships. Each node includes a local processing unit for biological data analysis, including multiomics and gene editing, privacy-preserving protocols for secure multi-party computation, a hierarchical knowledge graph for managing multi-domain biological relationships across spatial and temporal scales, and encrypted network connections. The system implements cross-species genetic analysis via phylogenetic integration, environmental response modeling through spatiotemporal tracking, and multi-scale tensor-based data integration with adaptive dimensionality control. This architecture enables research institutions to collaborate on complex biological analyses and genomic medicine applications while maintaining strict data privacy and security controls.

A federated distributed computational system enables secure biological data analysis and genomic medicine through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates classical numerical simulations with machine learning models for biological system analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for biological data analysis and privacy preservation protocols. The system implements cellular machinery assembly analysis, real-time patient data integration, and multi-modal image integration with spatiotemporal health data annotation. Through a distributed graph architecture, the system enables cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration with adaptive dimensionality control. The system implements real-time therapeutic response prediction through multi-modal data analysis, enabling research institutions to collaborate on complex biological analyses while maintaining strict data privacy controls.

The invention relates to the technical field of breeding planning, in particular to an intelligent breeding planning and decision-making method and system based on a large model. The method comprises the following steps: acquiring a multi-source breeding data set; constructing a structured breeding knowledge graph based on the multi-source breeding data set; performing breeding data association on the structured breeding knowledge graph according to a preset large model to generate a special breeding basic model; obtaining a breeding instruction input by a user; performing user semantic recognition on a breeding instruction input by a user to generate breeding semantic recognition data; inputting the breeding semantic recognition data into a breeding special basic model for breeding intention analysis, and generating user breeding intention data; and determining data information needing to be called based on the breeding intention data of the user, analyzing and screening to generate germplasm resource screening data and a breeding plan / breeding decision scheme. According to the method, the intelligence and operability of breeding planning are improved through integration of multi-source data, intelligent semantic recognition, combined genetic analysis and executable evaluation.

The invention discloses an interpretable machine learning genome prediction method and device, belongs to the technical field of combination of biological breeding, biological information and machine learning, and utilizes an advanced machine learning algorithm to perform parameter optimization in combination with biological prior information. Through processing of multi-source data (genome, transcriptome and epigenetic data), dynamic feature engineering (PCA and PHATE dimensionality reduction) and organic combination of various machine learning models, and an automatic parameter adjustment framework based on a grid search and sparrow search algorithm, genome prediction precision and calculation efficiency are significantly improved; meanwhile, the interpretability of the model is realized based on the SHAP value, the SNP site contribution is quantified, a reference is provided for precise breeding, and the method is suitable for animal and plant molecular breeding and medical genetic analysis, and can accelerate genetic analysis of high-value characters, assist precise breeding decision and disease risk prediction, and promote leap-forward development from experience breeding to intelligent breeding.

Agentic AI, Contextual AI, and Generative AI systems and methods are provided for quantitative or qualitative patient analytics, blood work analytics, medical image analytics (x-ray, ultrasound, CT Scan, MRI and the like), brain scan analytics, present or historical genetic analytics and the like, to be used to guide, assist or even replace some of these physician functions with improved patient outcomes or with an improved understanding of the source of ailments, the analysis of treatment efficacy or treatment side-effects for individuals or populations of individuals. Genetic information is used to create individualized or population-based analysis, diagnostics, or treatment plans with even better outcomes.

The invention discloses a tuna pond culture strategy optimization method and system based on meteorological influence analysis, and the method comprises the steps: collecting regional meteorological data and pond culture monitoring data, and constructing a multi-modal pond ecological feature vector; establishing a weather-water body dynamic coupling model by using a space-time diagram neural network, and generating water body situation response prediction information; a breeding regulation and control strategy is made based on the prediction information, and strategy self-adaptive optimization is achieved through real-time monitoring data; evaluating the anti-disaster capability of the tuna after the meteorological influence is finished, screening high-quality germplasm resources by combining genetic analysis, and generating an anti-disaster breeding scheme. According to the method, full-chain optimization from meteorological early warning to culture regulation and control to genetic breeding is achieved, and the climate toughness and economic benefits of tuna pond culture are comprehensively improved.

The invention discloses a rice drought resistance phenotype detection and genetic analysis method based on a phenotypic robot, which is characterized in that rice hyperspectral phenotypic character data is acquired based on the phenotypic robot, and the relative water content RWC of rice is predicted. Through high-frequency data acquisition, RWC (t) based on a time sequence is obtained, RWC (t) curve change characteristic indexes are calculated, the dynamic change process of a rice experiment material in the whole drought experiment period is quantified, the drought resistance index DTI of the rice experiment material is obtained, and the drought resistance of the rice variety is comprehensively evaluated. DTI as a user-defined phenotypic character can be used for whole genome association analysis (GWAS), and a key gene for controlling the drought resistance of rice is excavated. According to the invention, the problems of low efficiency and low flux of traditional rice phenotype detection are solved, phenotype-gene mining based on the mobile robot is realized, and a new way is expanded for improvement of rice genetic breeding.

The invention relates to the technical field of molecular biology, and discloses 74 core molecular marker sets for identifying cold resistance of pennisetum alopecuroides, a primer group, a detection reagent, a kit, a gene chip and application thereof. According to the method, core germplasm resources of pennisetum alopecuroides are systematically collected, and the GWAS population is constructed by utilizing multi-environment (different altitudes) conditions, so that wide genetic and phenotypic variation is fully covered, and a representative material foundation is laid for cold-resistant genetic analysis. In the aspect of phenotype evaluation, a method for determining the number of reviving and root cutting seedlings after natural overwintering by combining single-root double-stem-node oblique cutting planting is innovatively provided, a set of scientific and stable cold resistance and reproducibility evaluation system capable of being operated in a standardized mode is established, and the reliability and efficiency of phenotype identification are remarkably improved. The detection is not influenced by human and climate environments; the method realizes efficient and accurate early cold resistance identification of pennisetum alopecuroides, greatly improves the breeding screening efficiency and scale, and accelerates the breeding process.

The invention belongs to the technical field of molecular biology and plant molecular breeding, and particularly relates to a mustard core SNP molecular marker set and a screening method and application thereof. The invention provides a core SNP marker set (33) which is subjected to whole genome re-sequencing screening and experimental verification and is suitable for mustard germplasm resource identification and genetic analysis for the first time. The marker set is high in polymorphism and good in stability, covers the whole genome and can effectively distinguish different leaf mustard germplasms. By utilizing the core marker set and the matched KASP primer, the genetic typing of the leaf mustard germplasm resources can be quickly and accurately performed with high throughput, and the defects that the traditional morphological identification is time-consuming, labor-consuming and poor in accuracy are overcome. The marker set can be used for analyzing the genetic diversity, the population structure and the phylogenetic relationship of the leaf mustard germplasm resources, and a molecular basis is provided for collection, preservation and evaluation of the germplasm resources and breeding parent matching.

Agentic AI, Contextual AI, and Generative AI systems and methods are provided for quantitative or qualitative patient analytics, blood work analytics, medical image analytics (x-ray, ultrasound, CT Scan, MRI and the like), brain scan analytics, present or historical genetic analytics and the like, to be used to guide, assist or even replace some of these physician functions with improved patient outcomes or with an improved understanding of the source of ailments, the analysis of treatment efficacy or treatment side-effects for individuals or populations of individuals. Genetic information is used to create individualized or population-based analysis, diagnostics, or treatment plans with even better outcomes.

The invention discloses a chip for genome breeding and variety identification of tilapia mossambica. The invention provides a group of SNP (Single Nucleotide Polymorphism) marker combination for tilapia mossambica, which comprises 50000 SNP markers which are respectively SNP1-SNP50000 markers. According to the invention, the SNP marker combination related to economic characters of tilapia mossambica is integrated, representative sites with uniform coverage are selected, a biological probe for site typing is developed, and the site coverage, typing accuracy and GS accuracy of the SNP marker combination are basically consistent with those of re-sequencing; the method can be applied to the aspects of tilapia germplasm resource identification, genetic relationship identification, SNP typing, variety identification, molecular breeding, DNA fingerprint database construction, variety purity detection, germplasm resource genetic analysis, whole genome selective breeding, functional gene positioning, genetic map construction, genetic evolution analysis, whole genome association analysis and the like.

The invention provides a target character prediction method suitable for low-depth sequencing and a whole genome selection model suitable for low-depth sequencing, and relates to the technical field of biology. According to the target character prediction method suitable for low-depth sequencing and the whole-genome selection model provided by the invention, the target character of the to-be-detected sample can be accurately predicted under a limited data volume by combining low-depth whole-genome re-sequencing data with a machine learning technology, and the genome selection efficiency can be improved by matching the two; the time and economic cost of breeding or medical research are reduced, and the method is particularly suitable for large-scale genetic analysis scenes needing rapid iteration.

The invention belongs to the technical field of fish genetic identification, discloses a multi-site sal-family wild large yellow croaker identification method based on an SNaPshot technology, and particularly discloses a group of SNP (Single Nucleotide Polymorphism) molecular markers for screening a sal-family wild large yellow croaker group. The identification of the sal-family wild pseudosciaena crocea in the pseudosciaena crocea wild population sample can be realized only by a simple multiple PCR reaction system and SNaPshot typing sequencing. And automatic typing and identification of the wild large yellow croaker sample of the large sal ammoniac family can be realized only by operating on various genetic analysis instruments. Compared with the existing method for identifying the wild large yellow croaker in the sal family, the method has the advantages that the typing is more accurate, the operation is quicker, the detection flux is higher, and meanwhile, the accuracy rate and the cumulative exclusion rate are higher.

The invention belongs to the field of plant molecular genetics, and relates to a spirodela polymorphic microsatellite molecular marker primer and application thereof. The kit comprises 10 pairs of microsatellite primers which form 5 groups of duplex PCR systems, and the kit can be applied to population genetic diversity analysis of spirodela polyrhiza. The population genetic diversity analysis comprises the following steps: collecting a sample of spirodela polyrhiza to be identified; extracting spirodela polyrhiza genome DNA from the spirodela polyrhiza sample; taking the extracted DNA as a template, and carrying out PCR (Polymerase Chain Reaction) amplification on the DNA template by using the screened 10 pairs of microsatellite primers; carrying out electrophoresis detection on a PCR product, and then carrying out silver staining; the silver staining result is digitally analyzed, the UPGMA clustering analysis chart is drawn by genetic analysis software, the clustering analysis chart of the spirodela polyrhiza is identified according to the clustering analysis result, and the method has huge popularization and utilization value.

The invention belongs to the technical field of agronomy, and provides a high-throughput measurement method for the transverse diameter and the longitudinal diameter of a pineapple fruit based on image processing, which comprises the following steps: measuring the transverse diameter and the longitudinal diameter of the pineapple fruit by using a vernier caliper to obtain transverse diameter and longitudinal diameter data of the fruit, and constructing a training set and a test set; the method for automatically measuring the longitudinal stem and the transverse diameter of the pineapple fruit based on an open source computer vision library (OpenCV) is established, an efficient and accurate method is provided for high-throughput accurate evaluation of pineapple germplasm resource fruits, and accurate data support is provided for genetic analysis and gene mining of pineapple fruit size characters. And a good foundation is laid for the breeding of a new variety of the large-fruit pineapple.

The present disclosure provides a lineage tracking method comprising obtaining cells and sister cells thereof at different stages of differentiation, dividing the cells and sister cells thereof into at least two parts, one part for genetic analysis, the other part continuing to differentiate and performing genetic analysis, and performing cross-stage lineage analysis. The present disclosure also provides uses of the methods.

The present invention discloses a method for quantitatively assessing pathogen evolutionary diversity, comprising the following steps: 1) collecting core gene sequence data of pathogens based on specified pathogen species and performing quality control on the collected data to obtain high-quality sequence data; 2) then extracting features from the collected sequence data using Shannon entropy to obtain a feature vector for each sequence; 3) constructing a feature matrix based on the extracted feature data, and calculating the pathogen evolutionary diversity score using a clustering evaluation calculation method. By extracting sequence features, the present invention can achieve rapid and efficient evolutionary diversity assessment and transmission spillover risk analysis without the need for evolutionary tree analysis or modeling training. This method can provide technical support and reference for subsequent pathogen genetic analysis, related epidemic detection and monitoring, and the development of specific drugs and antibodies, and has a wide range of related applications.

The application relates to the technical field of molecular biology, and discloses a cold-tolerance identification method of Pennisetum based on a 65-core molecular marker set, a primer group and a gene chip and application thereof. The application collects core germplasm resources of the Pennisetum through a system, and constructs a GWAS population by using multi-environment (different altitudes) conditions, so that a wide genetic background and phenotype variation are fully covered, and a material foundation for genetic analysis of cold tolerance is laid. In the aspect of cold-tolerance phenotype identification, the application innovatively takes the chlorophyll content of leaves and the percentage of green leaves after natural low-temperature stress in autumn and winter fields as the cold-tolerance evaluation indexes, judges individuals with more than 40 excellent allelic variations as high-cold-tolerance varieties, and establishes a rapid and non-destructive phenotype screening method which can be implemented at the seedling stage.

The invention relates to a gene breeding value prediction method and device based on deep learning and a storage medium, and belongs to the technical field of biological information.The method comprises the steps that a target SNP site corresponding to each individual is extracted based on a Bayesian lasso model and phenotype vectors of the individuals; inputting the genotype matrix into a trained prediction model to obtain a predicted gene breeding value output by the prediction model; the KAN convolution model comprises two KAN convolution layers and a KAN linear layer; the KAN convolutional layer is constructed on the basis of the Kolmogorov-Arnod expression theorem. According to the gene breeding value prediction method based on deep learning provided by the invention, the KAN convolutional layer is constructed based on the Kolmogov-Arnod representation theorem, the nonlinear kernel function is adopted to replace the traditional linear function, and the KAN convolution operation is introduced, so that the prediction accuracy of the model is remarkably improved, the complexity of the model is reduced, and the prediction efficiency is improved. The method is suitable for genetic analysis and breeding prediction of complex characters of animals and plants.

The invention discloses a molecular marker closely linked with a thin-skinned muskmelon bacterial fruit blotch resistance gene and application thereof, and belongs to the field of muskmelon disease resistance identification. According to the method, a disease-resistant muskmelon strain and a susceptible muskmelon strain are subjected to interspecific hybridization, genetic analysis is carried out on BFB-resistant characters of muskmelons by utilizing an F2 group, a high-resistance gene pool and a high-susceptibility gene pool are constructed on the basis, and resistance candidate genes of the muskmelons are mined by utilizing a BSA-seq technology and a molecular marker technology. Research results can provide a research basis for disease resistance character gene mining and mechanism research, and the method has important scientific significance for screening and application of muskmelon disease-resistant germplasm resources. The molecular marker KASP988272 is developed according to the genotype difference between candidate gene parents, and the genotype identification accuracy of the molecular marker in an F2 group is as high as 80%. The developed molecular marker closely linked with the bacterial fruit blotch resistant gene is used for rapidly and efficiently identifying the bacterial fruit blotch resistant thin-skinned muskmelon variety, and a technical support is provided for selection of the bacterial fruit blotch resistant thin-skinned muskmelon variety.

The present invention provides a, preferably fully automated and / or multiplex, method for the simultaneous detection of a plurality of molecular genetic analytes in a collective and continuous reaction set up and at least one analyte specific oligonucleotide TX probes with a cleavable hydrolysis product for use in said method. Through the method the plurality of cleavable hydrolysis products is specifically released by a nuclease from the respective TX probes. Following a separation step, preferably in a capillary electrophoresis, for each hydrolysis product a clear and distinguishable from others signal is achieved. Each separated hydrolysis product respectively results in a signal enabling the qualitative and / or quantitative detection of each analyte comprising a molecular variant, e.g. single nucleotide polymorphism (SNP), deletion-insertion polymorphism (DIP) or other, respectively, that was targeted specifically by the plurality of TX probes. Preferably, the method is suitable to detect qualitatively and / or quantitatively small molecular variants as such SNP.

The present invention provides assay systems and methods for detection of copy number variation at one or more loci and polymorphism detection at one or more loci in a mixed sample from an individual.

The present invention belongs to the fields of biotechnology and agricultural science, and particularly relates to a liquid-phase chip detection method for the identification of Leymus chinensis varieties. The method of the present invention uses a 50K liquid-phase chip of the whole genome of Leymus chinensis for the identification of Leymus chinensis varieties. This method combines high-throughput liquid-phase chip technology with machine learning algorithms, and through high-throughput SNP detection, realizes the accurate detection of SNP sites in the whole genome, innovatively constructs an efficient and stable classification model, and significantly improves the accuracy and efficiency of variety identification. By screening specific SNP sites, combining population genetic analysis with the random forest algorithm, the reliability of the model is enhanced, and problems such as low efficiency of traditional methods are solved.

This invention describes systems, primers, kits, and methods for detecting microsatellite instabilities in biological samples. Signal data is received from a capillary electrophoresis genetic analysis instrument, wherein the signal data is determined based on the fluorescence of fragments comprising nucleic acid sequences amplified from the biological sample by polymerase chain reaction (PCR). The nucleic acid sequences correspond to multiple different microsatellite loci and are obtained using multiple PCR primers configured to side-join the multiple microsatellite loci of the biological sample. When the PCR primers are combined with the biological sample and subjected to PCR amplification, fluorescently labeled DNA fragments comprising the multiple microsatellite loci are generated. The microsatellite instabilities of the biological sample are classified using the fluorescence data obtained from the multiple fluorescently labeled microsatellite loci.

The present application belongs to the technical field of fish genetic identification, discloses a large yellow croaker wild and domestication population typing identification method based on 10 universal SNPs sites, and specifically discloses a set of SNP molecular markers for identifying large yellow croaker wild and domestication samples. The present application provides a set of SNP molecular markers for identifying large yellow croaker wild and domestication samples. By detecting the SNP molecular markers, the identification of large yellow croaker wild and domestication samples can be realized only by using a multiplex PCR reaction system and SNaPshot typing sequencing. The operation on various genetic analysis instruments can realize automatic typing and identification of large yellow croaker wild samples and large yellow croaker domestication samples. Compared with the current method for identifying large yellow croaker populations, the method is faster, has higher detection flux, and more importantly, has very high accuracy and cumulative exclusion rate.

The application provides a construction method of a humanized keloid mouse model, comprising the following steps: BAC plasmid construction and preparation; superovulation of experimental mice and collection of zygotes; pronuclear microinjection of zygotes; post-injection embryo transplantation; genotype identification; breeding and genetic analysis; and verification of construction results of the humanized immune system by immunohistochemistry and immunofluorescence. In the application, peripheral blood mononuclear cells treated by sCD27 and skin around keloids (homologous cells and tissues) are transplanted into NSG-MHC-DKO immune-deficient mice with overexpression of CD70 genes, so that HLA rejection of different homologous immune cells and tissues can be avoided, the internal microenvironment of keloids and the interaction between the internal microenvironment and the immune system can be restored to the maximum extent, and the influence of immune factors on the occurrence and development of keloids can be realized in vitro. The application first discovers and verifies that the activation of the CD27-CD70 axis can promote the occurrence and development of keloids, and provides a suitable animal model for the research and development of anti-keloid drugs, especially immunotherapy.

The invention discloses application of a GhGDSL102 gene in regulating and controlling the oil content of a plant, and belongs to the technical field of gene engineering. The GhGDSL102 gene is over-expressed in arabidopsis thaliana, the oil content of arabidopsis thaliana seeds is obviously reduced by 13.67%, and oleic acid C18: 1 is obviously reduced by 7.7%. After the GhGDSL102 gene is silenced, the oil content and oleic acid content of the cotton seeds are obviously increased by 9.99% and 7.57%. Meanwhile, three transgenic positive strains are obtained through overexpression of cotton, it is found through measurement of the oil content of cotton seeds that the oil content of the three overexpression strains is remarkably reduced by 11.70%, 9.14% and 19.25% respectively compared with that of a control group, and the oleic acid content is reduced by 5.54%, 7.09% and 6.29% respectively; the cottonseed oil content of the obtained homozygous edited strain is remarkably improved by 19.4%, and the GhGDSL102 is proved to be a gene for negatively regulating the cottonseed oil content.

The invention belongs to the field of crop heredity, and relates to application of a polyphenol oxidase gene. The invention discloses an InDel marker for rice brown glume traits, a regulatory gene and application of the InDel marker and the regulatory gene. Based on a brown glume mutant material in the background of Hunan early indica 42, genetic analysis shows that the mutation is dominant mutation, single-gene control and gene positioning find that the mutant gene is a polyphenol oxidase encoding gene (MSULocus is LOCOs04g53300), and the exon region of the mutant gene is deleted by 6bp (GCGACT), so that the material shows brown glume; the gene is knocked out from a brown glume mutant, and the glume color becomes normal; the method has the advantages that the GCGACTs are used as the raw materials, the GCGACTs are accurately edited in the wild type materials, the corresponding 6bp deletion (GCGACT) materials are obtained, the glumes of the GCGACTs are brown, and new gene resources and methods are provided for quickly and directionally cultivating novel rice materials and rice materials suitable for mechanized seed production of hybrid rice.