97 results about "Genetic data" patented technology

The invention belongs to the technical field of intelligent medical treatment, and provides an active health hierarchical management method based on multi-modal fusion and electronic device.The method comprises the steps that dynamic data and static data of a to-be-assessed person are obtained, physiological time sequence data and behavior time sequence data are input into a spatial-temporal feature extraction module, and the spatial-temporal feature extraction module is used for extracting spatial-temporal features of the to-be-assessed person; extracting a time sequence dependency relationship in the dynamic data, and identifying an abnormal feature sequence based on the time sequence dependency relationship by using a self-attention mechanism to obtain a dynamic risk feature; inputting the genetic data and the environmental exposure data into the static feature extraction module, and extracting static risk features based on a multi-head attention mechanism; fusing the obtained dynamic risk features and the static risk features by using a dynamic gating mechanism to obtain fused features; and transmitting the fusion features to the full connection layer to obtain health risk grade results of different time scales, and outputting a health hierarchical management file based on the health risk grade results.

The present invention generally relates to systems (100) and methods for generating, managing, and displaying non-fungible digital representations (102) of living, biological, and / or eukaryotic organisms (104). More specifically, the systems (100) and methods for generating, managing, and displaying non-fungible digital representations (102) include genetic material (106), such as single nucleotide polymorphisms. The systems (100) and methods can record genetic data from the genetic material (106) on a blockchain (114) and can generate and display the non-fungible digital representation (102) of the living, biological, and / or eukaryotic organisms (104) that are based on the genetic data on the blockchain (114).

According to an embodiment, disclosed is a system comprising a processor configured to receive an image of a breast of a patient and patient data comprising genetic data; extract features from the image and the patient data, using one or more machine learning models, wherein the features comprise a presence of a calcification and a calcification pattern to generate a breast calcification vector; augment the breast calcification vector with the genetic data; determine, using the machine learning models, a first risk for a breast cancer; a second risk to one or more organs of the patient, wherein the organs comprises one or more of heart, kidney, lungs, pancreas, and brain; predict, a third risk based on one or more of a healing response, a tumor flow and growth, inflammation and degeneration, a disease relapse, an adverse event, and a clinical response; and determine, an overall risk to the patient.

This application, in the field of intelligent medical technology, provides a proactive health stratification management method and electronic device based on multimodal fusion. The method includes: acquiring dynamic and static data of the person to be assessed; inputting the physiological time-series data and the behavioral time-series data into a spatiotemporal feature extraction module to extract the temporal dependencies in the dynamic data, and using a self-attention mechanism to identify abnormal feature sequences based on the temporal dependencies to obtain dynamic risk features; inputting the genetic data and the environmental exposure data into a static feature extraction module to extract static risk features based on a multi-head attention mechanism; fusing the obtained dynamic risk features and the static risk features using a dynamic gating mechanism to obtain fused features; passing the fused features to a fully connected layer to obtain health risk level results at different time scales, and outputting a health stratification management file based on the health risk level results.

The invention discloses an astragalus sinicus breeding genetic data mining system based on bioinformatics, and particularly relates to the technical field of data mining. The method comprises the following steps: acquiring and synchronizing original sequencing data of an astragalus sinicus host genome and a rhizobium symbiotic microorganism genome to generate a standardized genetic information data set; the method comprises the following steps: identifying sequence cross contamination sites between a host genome and a symbiotic microorganism genome, and eliminating genetic information interference of a microorganism source, so as to obtain purified host genome data without microorganism interference; analyzing the contribution degree of the microbial genome to the target character of the astragalus sinicus to obtain a microbial effect weight; integrating the purified host genome data and the microbial effect weight, constructing an astragalus sinicus character association analysis model, and positioning character association sites; and finally, screening candidate breeding markers based on character associated site information to obtain a precise breeding genetic marker set. According to the method, the accuracy and efficiency of milk vetch breeding are improved.

A system for generating a seed recommendation is disclosed. The system includes a processor, a display, and a memory. The processor may be configured to retrieve genetic data having a first dimensionality; generate embeddings corresponding to the genetic data, the embeddings having a second dimensionality lower than the first dimensionality; categorize the embeddings into one or more clusters, such that genetically similar seed products are assigned to the same cluster based on the embeddings of the genetically similar seed products; using agronomy data, assign additional seed products to the one or more clusters; generate a recommendation to a grower to plant a first seed categorized in a first cluster, when the grower has previously planted a second seed in the first cluster; and cause the display to display the generated recommendation to the grower.

The invention provides a Mendel randomization nonlinear causal inference method based on genome research, and relates to the technical field of biological genetic data analysis, and the method comprises the following steps: obtaining a tool variable, an exposure factor and a result variable, and carrying out data preprocessing to form a structured input matrix; respectively constructing a tool variable nonlinear model and a causal effect nonlinear model; calculating a loss function of the model, and adding a regularization item for joint optimization; optimizing model parameters by using a GBDT kernel method; updating the model based on the optimized model parameters and drawing a nonlinear response curve of the exposure factors and the result variables; replacing the tool variable with a pseudo tool variable to repeat the above steps to verify a causal inference result, and inferring a causal effect according to the verified causal inference result; according to the method, the limitation of assuming a linear relation and processing high-dimensional data in a traditional causal inference method can be overcome, and a more accurate, robust and interpretable causal inference result is provided.

The present invention provides a method and system for encrypted storage and inference of DNA barcodes of rare and endangered species, which relates to the field of encrypted inference technology, including: obtaining a rare and endangered species monitoring data set and a corresponding biological sample set; constructing an encrypted database based on the monitoring data set to obtain an anti-parsing database framework; encrypting the DNA barcodes based on the biological sample set to obtain an encrypted barcode set; generating a single valid sequence based on a verification sequence generated by the DNA sample to be identified; performing collaborative inference verification based on the single valid sequence and the encrypted barcode set to generate a species identification code; performing a comprehensive judgment based on the species identification code and the monitoring data set, and outputting an identification result by correlating morphological feature changes with geographic distribution density. By constructing an anti-parsing encrypted database framework and a functionally limited DNA barcode set, the present invention achieves active protection of rare species genetic data and solves the key hidden danger of sensitive biological information being used for poaching tracing.

The present invention relates to the field of precision medicine technology, specifically a system, method, and storage medium for interpreting precise tumor medication, including: a data acquisition module for collecting genetic data, clinical data, and drug response data of tumor patients from multiple data sources; a data standardization processing module for formatting and standardizing the data to ensure a consistent format and enable effective processing and analysis in subsequent modules; a genetic data analysis module for performing variation analysis based on the patient's genetic data, identifying gene mutation sites, and generating drug sensitivity analysis results; and a drug screening module for screening drugs that match the patient's gene mutation sites from a drug database based on the genetic data analysis results, and providing drug dosage recommendations. By adopting a technical solution that combines quantum optimization technology with genetic data analysis, the technical effect of significantly improving the accuracy of drug screening and dosage recommendations is achieved.

A method for practicing precision medicine comprising providing, to a blockchain platform, each of clinical data and genetic data, providing the blockchain platform, the blockchain platform having a first data structure comprising clinical data and a second data structure comprising genetic data, harmonizing the first and second data structures, creating at least one cohort based on the harmonized first and second data structures, and identifying at least one relationship between the clinical data and the genetic data in each of the at least one cohort.

The invention relates to a construction method of an arteriosclerosis risk assessment model, an arteriosclerosis risk assessment method, and application of an arteriosclerosis biomarker and a reagent for detecting the biomarker in preparation of a kit for predicting, preventing and / or treating arteriosclerosis. The construction method of the arteriosclerosis risk assessment model comprises the steps that correlation analysis is conducted based on genetic data and background data of a population to obtain correlation significance values corresponding to SNP loci, and the genetic data comprises the SNP loci and genotypes of the SNP loci; the arteriosclerosis risk assessment model is constructed on the basis of the SNP loci and the associated significance values corresponding to the SNP loci, and the arteriosclerosis risk assessment model comprises one or more of the SNP loci shown in the table 1. The arteriosclerosis risk assessment model obtained according to the construction method provided by the embodiment of the invention considers the specific genetic background, lifestyle, environmental factors and the like of Chinese population, the AUC of the obtained model reaches 0.693, and the arteriosclerosis risk of an individual to be analyzed can be accurately and effectively assessed.

Methods arc provided for making a plant genome having a desired nucleic acid sequence. One example method includes identifying genomic segments of a plant that include at least one desired nucleic acid sequence, using a trained neural network, which is biology-informed through first pre-training connections between a genetic data layer and at least one intermediate layer and second pre-training connection between the at least one intermediate layer and a phenotypic layer. The method also includes recombining the at least one at least one desired nucleic acid sequence with a second nucleic acid sequence to form a target plant genome.

An example embodiment may involve receiving, by way of a user interface of a computing system, an indication of a healthcare patient profile and further input; based on the indication of the healthcare patient profile, obtaining, by the computing system, health records and genetic data of a patient associated with the healthcare patient profile; based on the health records and the genetic data of the patient and the further input, generating, by the computing system, a natural language prompt, wherein the natural language prompt contains instructions to generate a healthcare document for the patient; providing, by the computing system, the natural language prompt to a generative natural language model; receiving, by the computing system, the healthcare document from the generative natural language model; and providing, by way of the user interface, a version of the healthcare document.

This invention discloses an automatic control method for the width of hot-rolled steel coils. First, width data from the slab production process is collected and its average value is calculated. Then, the actual steel pulling amount for finishing rolling and coiling is calculated, and an adaptive genetic table for steel pulling amount is created. After the slab is drawn, the adaptive genetic table for steel pulling amount is queried to obtain genetic data. If retrieval fails, the process returns and a default width allowance value is selected. If retrieval is successful, the predicted coiling width allowance value is calculated for the strip to be rolled. Next, a recommended width allowance correction value is calculated. Then, the width allowance of the electrical equipment is set according to the default width allowance value or the recommended width allowance correction value to control the slab rolling process. Finally, data is stored and the adaptive genetic table for steel pulling amount is updated. This invention improves the finished product width accuracy through staged stable control of steel pulling amount, significantly reduces the proportion of manual intervention, significantly increases the width hit rate at the roughing mill exit, and improves the stability of production line width control.

The present disclosure relates to analyzing genetic data. In one arrangement, a method operates on input data comprising the strength of association between one or more phenotypes, including a target phenotype, and a plurality of genetic variants. A fine-mapping algorithm is applied to all or a subset of the input data to identify one or more independent phenotype-variant associations. For each association, a set of one or more fine-mapped variants is identified. A fine-mapping prediction model is calculated based on the input data and the set of fine-mapped variants. The effect of the set of fine-mapped variants on the target phenotype is subtracted from the input data to obtain residual association data. A machine learning algorithm is applied to the residual association data to identify further predictive correlations between the target phenotype and the plurality of genetic variants.

The embodiments of the present application provide a method, system, electronic device and storage medium for processing genetic data, which belongs to the field of genetic analysis technology. The method obtains the genotype data of the individual to be tested, and determines multiple associated genes of the individual to be tested based on the genotype data and the single nucleotide polymorphism data associated with the target risk task; queries the associated gene features corresponding to each associated gene in the pre-constructed gene feature library, and the gene feature library includes embedded representations of different genes constructed based on the embedding model; the embedding model is obtained by self-supervised learning of multiple gene expression profiles of the same species as the individual to be tested; integrates the features of multiple associated gene features to obtain individual features; calls the prediction model to process the individual features to obtain the risk score corresponding to the individual to be tested and the target risk task. This method can improve the accuracy of genetic data processing.

This invention discloses a method for evaluating the effectiveness of stock enhancement and release based on microsatellite markers. The method involves collecting a dataset of recaptured samples of released species; obtaining microsatellite genetic data of released broodstock, released fry, and recaptured samples; performing consistency processing on the microsatellite genetic data of released broodstock to supplement the virtual parent microsatellite genotype database; generating an allele contribution probability weight matrix to obtain an allele contribution probability dataset of recaptured samples; generating a basic matching list to construct a contribution weight dataset; and outputting the stock enhancement and release contribution rate evaluation results and storing them in a stock enhancement and release evaluation database. This invention makes the stock enhancement contribution rate no longer simply dependent on the number of matches, but comprehensively reflects the true contribution of recaptured individuals in the overall genetic background, thus improving the accuracy and robustness of stock enhancement and release effectiveness evaluation from an algorithmic perspective.

A system and method for generating and displaying a heat map is provided, comprising: a heat map generating computing device including a processor programmed to receive trap data for a plurality of pest traps at a geographic location, the trap data including pest genetic data, the trap data including current and past pest pressure values ​​for each of the plurality of pest traps; receive weather data for the geographic location; receive image data for the geographic location; apply a machine learning algorithm to generate predicted future pest pressure values ​​for each of the plurality of pest traps; generate a first heat map at a first time point and a second heat map at a second time point; transmit the first and second heat maps to a mobile computing device; and display the time lapse heat map in a user interface on the mobile computing device.

The application discloses a kind of integrated hybrid information purebred genome prediction method, belong to animal genetic breeding and propagation technical field.The population genetic dataset comprising purebred population A, B and hybrid population CB is constructed, and the population genetic differentiation is verified by principal component analysis and genetic differentiation index;After the genotypes of purebred and hybrid population CB are phased, the ancestral information inference method is used to determine the origin information of each allele and process missing sites;Based on the origin information of allele, additive effect partial kinship correlation matrix and dominant effect partial kinship correlation matrix are constructed;The above matrix is integrated into three-trait mixed linear model, forming the prediction model that simultaneously incorporates purebred and hybrid population genetic information;The application distinguishes the breed origin of allele and quantifies its additive and dominant effect, significantly improves the prediction accuracy of purebred genome breeding value, is suitable for the traits difficult to directly determine in purebred population, and provides reliable technical support for animal breeding.

The invention relates to the technical field of medical artificial intelligence prediction, and discloses a method for predicting the total lifetime of bladder cancer through a multi-modal fusion deep learning model. The method includes quality control and normalized preprocessing by collecting clinical, image and genetic data of a patient. Then multi-modal features are extracted, and high-value features are screened out through feature importance evaluation to form an enhanced feature set; a feature interaction network is adopted to learn a complex dependency relationship among different modal features, and deep fusion is realized to generate integrated feature representation. And finally, constructing a depth prediction model based on the representation, and capturing a mapping relation between the depth prediction model and the total lifetime. Through the deep fusion and feature screening technology, multi-modal information is integrated, the defect that a traditional method neglects interaction between modals is overcome, and the accuracy and reliability of predicting the total lifetime of the bladder cancer patient are improved.

This method for predicting T cell activation via peptide-MHC includes the following steps: wherein the analytical device: receives genetic data from a patient; identifies, based on the genetic data, the first amino acid sequence of the major histocompatibility complex (MHC) and the second amino acid sequence of an antigen produced by tumor cells; generates a matrix indicating the interrelationship between the first and second amino acid sequences in units of individual amino acids; and inputs the matrix into a trained neural network model to determine whether T cells secrete at least a threshold amount of cytokines due to the binding of MHC to the antigen.

A computer implemented method includes retrieving data from a patient's updated electronic medical record relevant to a diagnosis of a disease. The data includes medical images and at least one of the patient's demographic data, morbid symptoms, vital signs, medications, surgery history, family medical history, genetic data, laboratory test data, diseases records, allergies, and medical insurance information. The method includes generating available treatment path options including at least one surgical treatment or intervention, using at least one model to predict at least one implication for each of the available treatment path options based on the data and simulated adverse outcomes of the at least one surgical treatment or intervention simulated by predictive models including a biomechanical model based on the medical images. The method includes interactively updating and displaying a decision tree including the available treatment path options each with a corresponding at least one objective function.

The disclosure relates to analysing genetic data. In one arrangement, a method operates on input data comprising strengths of association between one or more phenotypes including a target phenotype and a plurality of genetic variants. A fine-mapping algorithm is applied to all or a subset of the input data to identify one or more independent phenotype-variant associations. A set of one or more fine-mapped variants is identified for each association. A fine-mapping predictive model is calculated on the basis of the input data and the set of fine-mapped variants. The effect on the target phenotype of the set of fine-mapped variants is subtracted from the input data to obtain residual association data. A machine learning algorithm is applied to the residual association data to identify further predictive correlations between the target phenotype and the plurality of genetic variants.

Aspects of the disclosure relate to an algorithmic- or machine learning-based iterative seed product recommendation system. A seed recommendation platform may analyze seed genetics data, environmental data, seed similarity parameters, fitness parameters, etc., to generate an initial seed product recommendation. Based on user feedback, the seed recommendation platform may iteratively update various modules used for generating seed product recommendation and provide revised seed product recommendations.

Disclosed herein is a system and method for increasing the fidelity of measured genetic data, for making allele calls, and for determining the state of aneuploidy, in one or a small set of cells, or from fragmentary DNA, where a limited quantity of genetic data is available. Poorly or incorrectly measured base pairs, missing alleles and missing regions are reconstructed using expected similarities between the target genome and the genome of genetically related individuals. In accordance with one embodiment, incomplete genetic data from an embryonic cell are reconstructed at a plurality of loci using the more complete genetic data from a larger sample of diploid cells from one or both parents, with or without haploid genetic data from one or both parents. In another embodiment, the chromosome copy number can be determined from the measured genetic data, with or without genetic information from one or both parents.

A health and wellness system for a non-human subject comprising analyzing genetic data and phenotypic data of the non-human subject with a machine learning algorithm and making a recommendation or recommendation for products or activities for the non-human subject.

The invention discloses a multivariate intelligent evaluation and career planning consultation system based on multi-modal data fusion, and belongs to the crossing field of artificial intelligence and education evaluation. The system comprises a multi-modal data acquisition module, a preprocessing module, a multivariate intelligent evaluation module, a cross combination analysis module, a dynamic updating module and a personalized recommendation module. The acquisition module integrates structured data (questionnaires, academic data and genes) and unstructured data (behaviors, voices and images); the preprocessing module cleans, converts and fuses data; the evaluation module constructs an'intelligence-capability 'secondary model, and quantifies 8 items of intelligence and 40 items of core capability; the cross analysis module generates a potential label; the dynamic updating module iterates the result in real time; and the recommendation module outputs interest, academic, professional and career planning suggestions. The system solves the problems of single traditional evaluation data, rough evaluation, poor dynamics and the like, and improves the planning precision and timeliness.

The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating a matched-data-link tree defining relationships among individuals according to genetic data. For example, the disclosed systems can determine matches of matches using a novel matching database structure. The disclosed systems encode integers based on data matches of a data identifier and populate a match database with the integers. Correlating data match integers for a data identifier with data match integers for data matches, the disclosed systems generate matches of matches. The disclosed systems can generate relationship clusters from matches of matches. For example, the disclosed systems compare sets of data matches to discover groups of data matches with stronger data-match levels. The disclosed systems can generate and populate a data-link tree from a relationship cluster. In addition, the disclosed systems can merge one or more data-link trees into a single data-link tree.

Disclosed herein is a system and method for increasing the fidelity of measured genetic data, for making allele calls, and for determining the state of aneuploidy, in one or a small set of cells, or from fragmentary DNA, where a limited quantity of genetic data is available. Poorly or incorrectly measured base pairs, missing alleles and missing regions are reconstructed using expected similarities between the target genome and the genome of genetically related individuals. In accordance with one embodiment, incomplete genetic data from an embryonic cell are reconstructed at a plurality of loci using the more complete genetic data from a larger sample of diploid cells from one or both parents, with or without haploid genetic data from one or both parents. In another embodiment, the chromosome copy number can be determined from the measured genetic data, with or without genetic information from one or both parents.