60 results about "Genetic data" patented technology

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.

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.

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.

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.

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.

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 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.

The application provides a missing genotype filling method based on an automatic encoder sample matching, realizes low-cost and accurate filling of missing genotypes, and can provide more accurate genetic data support for various genetic analysis work. In the application, the genotype information value of each sample at each position in the target data file is converted and finally encoded into a one-hot encoding, a training set and a test set are divided, and then a convolutional denoising autoencoder model is constructed. The application uses an automatic preprocessing strategy to segment the sample data set participating in filling, reduces the device memory occupation, so that the user can successfully perform high-precision genotype filling using a low-cost device. The application has high filling precision, simple and reliable model structure, high training efficiency, and has a wide application prospect in the field of genetic sequence analysis, and can be used for subsequent biological whole genome association analysis and whole genome selection work.

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.

Provided herein are methods of producing a plurality of genetically modified cells that include introducing a nucleic acid molecule including a plurality of index sequences into a cell comprising a synthetic landing pad, wherein each of the plurality of index sequences includes a first portion of a sequence and the synthetic landing pad includes a second portion of the sequence. The method further includes generating a plurality of cells that include the synthetic landing pad and the nucleic acid molecule including the plurality of index sequences and integrating one of the plurality of index sequences into the synthetic landing pad in each of the cells, thereby linking the first and second portions of the sequence. The linked first and second portions of the sequence result in a functional gene and cells including the integrated index sequence are selected based on presence or activity of the functional gene.

The invention relates to a risk prediction method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining to-be-processed multi-source data which comprises genetic data, family medical history data, phenotype data, individual behavior data, environmental factors and medical data; performing graph construction based on the to-be-processed multi-source data to obtain a dynamic crowd health evolution graph; and based on the dynamic crowd health evolution graph, performing risk prediction by using a trained network prediction model to obtain a risk prediction result. The risk prediction accuracy can be improved.

The application discloses a biological information-based genetic data mining system for Astragalus sinicus breeding, and particularly relates to the technical field of data mining; the system is characterized in that: raw sequencing data of a host genome of Astragalus sinicus and a symbiotic microorganism genome of rhizobium is collected and synchronized to generate a standardized genetic information dataset; sequence cross-contamination sites between the host genome and the symbiotic microorganism genome are identified, and genetic information interference from the microorganism is removed to obtain purified host genome data free of microorganism interference; the contribution of the microorganism genome to target traits of Astragalus sinicus is analyzed to obtain a microorganism effect weight; the purified host genome data and the microorganism effect weight are integrated to construct a trait association analysis model of Astragalus sinicus and locate trait association sites; finally, candidate breeding markers are screened based on the trait association site information to obtain a precise breeding genetic marker set; the application improves the accuracy and efficiency of Astragalus sinicus breeding.

A method for analyzing various regions of a genome at different resolutions, is disclosed herein, wherein the method comprises: producing, a whole-genome sequencing (WGS) library, wherein the WGS library is created from at least one of: DNA, RNA, and TNA; enriching, the WGS library, for each of one or more regions of interest; producing, a final sequencing library, wherein a first grouping of genomic regions are represented at a higher coverage than a second grouping of genomic regions; applying, NGS sequencing, to the final sequencing library, creating genetic data; and analyzing, the genetic data, to identify genetic markers.

Described herein are systems and methods and systems for preimplantation genetic testing of an embryo derived from consanguineous parents (PGT-C). The methods involve receiving embryonic genetic data and determining a proportion of the genome in long runs of homozygosity for the entire genome (global F value) as well as for one or more regions of interest within the genome (localized F values). The localized F values are weighted based on one or more factors relating to genetic viability and genetic disorders of the embryo. The global F value and weighted localized F values may be integrated with other genetic data to predict relevant risk scores.

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.

This invention discloses a non-invasive identification model for endometriosis staging based on brain functional connectivity. By integrating brain functional connectivity phenotypes derived from resting-state functional magnetic resonance imaging (fMRI) data with endometriosis genetic data, and through site matching, screening of effective genetic instrument variables, and analysis of staging-related features, the method accurately identifies staging-specific brain functional connectivity markers, achieving non-invasive and accurate identification of EMs staging. This enables non-invasive auxiliary identification of EMs staging and preoperative risk stratification assessment, reducing the impact of confounding factors and reverse causality on staging judgment, and providing quantifiable central nervous system indicators for clinical preoperative staging.

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.

A method of analyzing genetic data for an organism is disclosed that includes receiving a plurality of input units. Each input unit includes information about an association between a genetic variant in a region of the genome and a phenotype or phenotype combination. The method includes performing an iteration that includes determining, for each variant, whether the variant is causal to the phenotype or phenotype combination based on the input units. If the variant is causal to the phenotype or phenotype combination, a sampled effect size of the variant on the phenotype or phenotype combination is determined based on the input units and the information about the correlation between the variants in the region. For each variant, a predicted effect size of the variant on the phenotype or phenotype combination is determined based on an average of posterior effect sizes calculated across the iterations of the sampled effect sizes or using the sampled effect sizes.