172 results about "Genetic data" patented technology

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.

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.

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 belongs to the field of artificial intelligence and digital medical treatment, and relates to a drug recommendation method, which comprises the following steps: preprocessing medical record data and gene data according to a preset data standard specification to obtain standard medical record data and standard gene data, and fusing the standard medical record data and the standard gene data through a health analysis model for prediction to obtain a drug recommendation result. And analyzing the health analysis result, the standard medical record data and the standard gene data through a drug recommendation model to obtain a drug recommendation scheme. The invention further provides a drug recommendation device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the medical record data and the gene data can be stored in a block chain. According to the method, the efficiency and accuracy of drug recommendation can be improved, the personalized level of drug recommendation is remarkably improved, drug treatment is more targeted and effective, and the drug use safety of patients is improved.

The invention belongs to the technical field of biological feature recognition, and relates to a gene text data amplification method based on a generative adversarial network, which comprises the following steps: acquiring original data, and converting the data into a format suitable for GAN processing; then constructing a GAN, inputting the processed data into the GAN for training, and generating a new gene sequence by using the trained GAN; the GAN comprises a generator, a discriminator and a feature discriminator; the generator is used for mapping random noise to a gene feature space and generating a new gene sequence similar to real data distribution; the discriminator is used for evaluating the authenticity of the generated data, and the feature discriminator is used for evaluating the consistency of the generated data and the original data in basic group distribution, so that the generated data is consistent with the original gene data in overall feature. The data generated by the method is close to real data in the overall structure, and is highly consistent in detail features, so that the reliability of the amplified data in practical application is ensured.

The invention provides a liver cancer patient diagnosis data retrieval precise diagnosis and treatment system based on big data, and relates to the technical field of medical diagnosis. The liver cancer patient diagnosis data retrieval precise diagnosis and treatment system based on big data comprises a case database module, a clinical decision support system, a deep learning module, an early warning visualization module, a data analysis and processing module, a multi-modal data fusion module, a data collection and storage module, a feature selection and selection module and a data acquisition module. Image data, genome data, pathological data and electronic medical record data are integrated through multi-modal data fusion, comprehensive analysis is carried out, the accuracy of risk prediction is improved, treatment schemes are dynamically adjusted according to specific conditions and treatment responses of patients, personalized risk prediction and treatment scheme recommendation are achieved, and the risk prediction and treatment scheme recommendation efficiency is improved by combining gene data. Medicine selection and dosage are optimized, the treatment risk is reduced, the treatment effect is improved, doctors can understand the basis of system decision making, and the credibility of clinical application is improved.

Processes that determine transcriptional regulation from genetic sequence data are described. Generally, computational models are trained to predict transcriptional regulatory effects, which can be used in several downstream applications. Various methods further develop research tools, develop and perform diagnostics, and treat individuals based on identified variants.

The invention discloses an SNP (Single Nucleotide Polymorphism) marker-based rice disease resistance character whole genome association analysis method, which relates to the technical field of biology, and is characterized by comprising the following steps: selecting a representative genetic recombination population, carrying out high-density SNP genetic typing on the population, and combining recombination event positioning and linkage analysis to obtain the disease resistance character whole genome association analysis of rice. Obtaining genetic parameters reflecting recombination frequencies of different genome segments; on the basis of the obtained original genetic data, parent and offspring genotype information is further integrated, fine positioning and statistics are carried out on recombination breaking points, and a high-resolution genetic map is constructed. By constructing a high-resolution genetic map and combining feature engineering and machine learning, accurate identification and dynamic SNP encryption of a high-recombination region are realized, the marker coverage and associated signal capture capability of the region is enhanced, the functional site leak detection risk is reduced, and the detection efficiency is improved. The positioning accuracy and the molecular breeding application effect of the rice disease resistance character related candidate gene are improved.

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.

Provided herein are personalized wellness systems for non-human subjects, such as a companion animal, that utilize a machine learning model to analyze genetic data and phenotypic data of the non-human subjects and make a product or behavioral recommendation for the non-human subjects based, at least in part, on the analysis. The personalized wellness systems may include an Application (App) or website for use by a guardian or medical healthcare professional of the non-human subject to monitor the health and wellness of the non-human animal and receive notifications regarding the recommended products or behavioral modifications. The machine learning model may also analyze environmental data, behavioral, clinical data, and activity data to generate new recommendations or refine existing recommendations for the non-human subject.

The invention relates to the technical field of bioinformatics, in particular to an embryo gene detection and analysis system for data storage. Comprising a signal standardization unit, a data preprocessing unit, a storage optimization unit, a machine learning analysis unit, an intelligent report unit and a safety calculation unit. Through intelligent segmentation, parallel computing and SIMD optimization of the data preprocessing unit, the processing efficiency and quality of original sequencing signals are greatly improved, and meanwhile, the machine learning analysis unit utilizes a generative adversarial network and auto-encoder fusion model, a transfer learning and meta-learning strategy and a multi-modal fusion technology; the deep mining of gene data features, the improvement of model generalization ability and the effective integration of multi-source data are realized, so that the embryo health is accurately evaluated, the disease prediction accuracy is improved, and the accuracy, robustness and clinical application value of the system are remarkably enhanced.

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).

The invention relates to the technical field of image processing and recognition, and provides an Alzheimer's disease multi-mode classification method based on electroencephalogram, brain image and genetic information, and the method comprises the steps: obtaining electroencephalogram data, brain image data and genetic data of a subject, and carrying out the corresponding preprocessing operation; performing feature extraction on the preprocessed electroencephalogram data, brain image data and gene data to obtain electroencephalogram signal features, brain image features and AD susceptibility features of the subject; respectively carrying out corresponding standardization processing on the features of the three different modes of the subject, then carrying out feature selection, eliminating irrelevant features and redundant features, and finally carrying out feature fusion to obtain fused features; according to the method, binary classification and multi-classification are carried out on the basis of the features of the three different modes after feature fusion and feature selection, and a disease discrimination result of the subject is obtained on the basis of a binary classification result and a multi-classification result, so that the Alzheimer's disease recognition precision is effectively improved.

Systems and methods for generating a likelihood score that is indicative of a likelihood of a pet developing at least one of a disease, a clinical condition or other trait are disclosed. An example method may include: receiving, at a server system, pet data associated with the pet, wherein the pet data includes genetic data and breed data; generating, using a processor of the server system, a likelihood score for the pet associated with the clinical condition by applying the pet data to a trained machine learning model, wherein the trained machine learning model is trained to predict likelihood scores for the clinical condition; and causing, by the processor of the server system, a visual representation of the likelihood score to be displayed on a user device, wherein the visual representation includes an indication of the likelihood of the pet developing the clinical condition. Other aspects are described and claimed.

The invention relates to a genetic variation interpretation method and device, computer equipment, a readable storage medium and a program product, and the method comprises the steps: obtaining to-be-interpreted genetic data, determining the multi-modal data of a reference corresponding to at least one variation site in the to-be-interpreted genetic data based on a pre-constructed search database, and obtaining the multi-modal data of the reference corresponding to at least one variation site in the to-be-interpreted genetic data; and according to the at least one variation site and the multi-modal data of the corresponding reference, determining a corresponding variation rating evidence and a support basis of the variation rating evidence, and based on an upgrading and degrading strategy corresponding to the variation rating evidence, carrying out upgrading and degrading processing on the variation rating evidence according to the support basis of the variation rating evidence. And obtaining a variation rating result. According to the method, full automation of ACMG literature evidence rating is achieved by automatically reading literature content, and the working efficiency of genetic variation interpretation can be remarkably improved; and the accuracy and the reliability of the final variation rating result can be remarkably improved by performing upgrading and downgrading processing on the variation rating evidence.

The invention relates to the technical field of medical instrument quality management, in particular to a medical instrument monitoring method and system, and the method comprises the steps: firstly generating design gene data containing a model code, a physical unclonable fingerprint and a quantum public key at a design freezing node, and writing the design gene data into a Twindow-DNA chip; in the manufacturing stage, process parameters and scattering optical data are synchronously collected, an optical domain super-vector is constructed and spliced with process embedding to form manufacturing embedding characteristic data, and the manufacturing embedding characteristic data are encrypted and then sent to a digital twinborn body; performing twin calculation on a normalized residual error, generating a residual error vector through vector holographic convolution and multi-stage diffusion denoising when the normalized residual error exceeds a threshold value, inputting the residual error vector into a super-vector converter to obtain defect semantic data, and writing the defect semantic data into a three-division hypergraph account book which takes a quantum public key as an index by using zero knowledge commitment; and calling the hypergraph neural network and the neural relationship inference model according to a fixed time interval, outputting correction-prevention data, writing the correction-prevention data back to a manufacturing execution system, and meanwhile, performing hash chain writing on the correction-prevention data to form a quality closed loop and support operation decision and regulation delivery.

The invention provides an Alzheimer's disease diagnosis system based on image gene data fusion, which innovatively introduces a decoupling representation technology, divides features of multi-modal data into global representation and local representation, retains unique information of each data modal by respectively extracting global features and local features, and improves the diagnosis accuracy of the Alzheimer's disease. Efficient fusion of shared information is realized; through the innovative multi-modal feature fusion method, the accuracy and stability of complex disease prediction and diagnosis are remarkably improved, and the problem that cross-modal features and modal internal features cannot be captured at the same time in the prior art is solved.

The invention provides a Kawasaki disease coronary artery disease risk dynamic prediction algorithm and an automatic upgrading system. The Kawasaki disease coronary artery lesion risk dynamic prediction algorithm and the automatic upgrading system comprise the following steps: a, acquiring clinical data of a Kawasaki disease child patient, including data of disease date, sex, month age, serum sodium level, D dimer, erythrocyte distribution width, prothrombin time and C-reactive protein biomarker; according to the Kawasaki disease coronary artery disease risk dynamic prediction algorithm and the automatic upgrading system, through combination of clinical features and gene data of Kawasaki disease children patients, a plurality of machine learning algorithms are used, and an efficient and accurate CAL risk prediction model is constructed. The model combines a plurality of key clinical indexes such as serum sodium level, CRP and D dimer and gene data such as SNP markers such as SIGLEC8-CEACAM18 and PPP1R1C, and the sensitivity and specificity of risk prediction are remarkably improved.

The invention relates to the technical field of data analysis and interpretation, in particular to a colorectal cancer genetic data analysis and interpretation system, which comprises the steps of collecting various types of genetic data from a colorectal cancer patient, performing primary processing, identifying gene variation and biomarkers related to colorectal cancer, and analyzing and interpreting the colorectal cancer genetic data. The method comprises the following steps of: extracting potential genetic characteristics and correlation between the potential genetic characteristics and colorectal cancer onset risks, performing function annotation and interpretation on identified gene variation and biomarkers, and generating personalized treatment suggestions, including targeted treatment schemes, drug selection and prognosis prediction. According to the invention, the specific effect of the gene in colorectal cancer pathogenesis and the influence of the gene on disease progression are deeply analyzed, a targeted treatment scheme, drug selection and prognosis prediction are generated, and precise medical development of colorectal cancer is promoted. The high efficiency and accuracy of the system significantly improve the quality of genetic data analysis, and an important innovative tool is provided for early diagnosis, prognosis evaluation and personalized treatment of colorectal cancer.

The invention discloses a gene feature extraction method in gene data based on sequence search, and relates to the technical field of biological information. The problems that gene data is wide in source and diversified in format, data processing is extremely complex, data of different formats are difficult to integrate and analyze, and the accuracy and efficiency of feature extraction are affected are solved. According to the method, gene data are comprehensively preprocessed, low-quality and redundant parts are removed, reliable data quality is ensured, data distribution and data conversion are performed at the same time, high-similarity gene feature regions are accurately screened through sequence comparison, importance weights of gene features are determined by using clustering group features, key features are reasonably screened, and the accuracy of gene feature extraction is improved. Data dimensions are effectively reduced, the feature extraction efficiency and accuracy are improved, local and global features are fused to construct a comprehensive feature vector, the feature expression ability is enhanced, a screening result is verified by constructing a test data set, and the accuracy and reliability of the gene feature extraction method are continuously improved.

Methods of predicting a chromatin state metric associated with a genomic region in a sample are provided. The methods comprise: receiving sequence data comprising genetic data and epigenetic data indicative of the presence of one or more epigenetic bases including methylated cytosine and hydroxymethylated cytosine at one or more genomic positions, the one or more epigenetic bases, the genetic data and epigenetic data obtained from a single assay; and predicting for each base or set of bases of the genomic region and using the sequence data associated with the genomic region, a value of the chromatin state metric, wherein the predicting is performed using a machine learning model.

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.

The invention discloses a cancer patient gene methylation feature screening method based on DDPG reinforcement learning. According to the method, a depth deterministic strategy gradient algorithm (DDPG) is combined with gene methylation data, and important features closely related to cancer classification are accurately screened out from massive gene methylation data. The system dynamically selects gene methylation features through effective exploration of a continuous state space and an action space, and optimizes a feature selection strategy through a reward mechanism of reinforcement learning. According to the method, the accuracy of a cancer classification model can be remarkably improved, and powerful support can be provided for early detection and personalized treatment of cancers by analyzing the screened features. According to the method, the challenge of high-dimensional gene data feature selection is effectively solved, the data processing efficiency in biomedical research is improved, and the method has wide application prospects and practical value.

The invention relates to the technical field of data mining, in particular to a brain tumor multi-dimensional data analysis method and system, and the method comprises the following steps: based on a brain tumor multi-dimensional data set, integrating image data, clinical data and gene data, distributing an initial weight of each type of data, and matching the criticality of each type of data in a target diagnosis task. According to the method, various types of data are ensured to be processed according to the actual contribution of the data in judgment through weight optimization, so that the brain tumor data set better meets clinical requirements, the sensitivity and specificity of brain tumor judgment are improved, mining of complementarity among different data sources is improved through refined feature fusion, and the accuracy of brain tumor judgment is improved. Pathological features which are difficult to find through a single data source become more obvious, clustering analysis and data quality evaluation technologies are adopted, data processing comprehensiveness is ensured, more reliable support is provided for judgment and decision, the whole analysis process is optimized, and data are increased in quantity and improved in quality remarkably.

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.

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.

Systems and methods for predicting the epidemic rate of loss of heterozygosity (LOH) in a target cell population, where the system includes a memory and a processor configured to receive genetic data of a first reference cell population. The processor is configured to sequence the genetic data of the first reference cell population to obtain first reference data; identifying and removing heterozygous variant positions with unbalanced allele expression in the first reference data to generate second reference data; mapping an identifier of each cell of the target cell population to the second reference data; and applying the mapping identifier for each cell of the target cell population to a supervised machine learning model. The processor is further configured to receive one or more outputs from the model, at least one of the one or more outputs comprising an LOH of the target cell population.

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 relates to the technical field of artificial intelligence prediction and evaluation, and discloses a Mendel randomization evaluation system and method for risk of social activities to breast cancer, the system comprises a data acquisition module, a data processing module, a double-sample Mendel randomization analysis module and a result output module; the data acquisition module serves as an information source of the whole system, has the capability of accurately collecting data from a diversified and high-quality data source, and can retrieve genetic polymorphic data related to social isolation and interaction from a public or private database. According to the system and method for evaluating the risk of the social activity on the breast cancer through Mendel randomization, by integrating multi-source genetic data and applying a double-sample Mendel randomization analysis technology, the problems of confounding factors and inherent deviation in traditional epidemiological research are solved, and the risk of the social activity on the breast cancer is evaluated. According to the system, the potential causal relationship between the breast cancer and social activities such as social isolation and social interaction can be accurately evaluated, and a reliable analysis result is provided for a user.