362 results about "Biological data" 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 collaboration across multiple institutions for biological data analysis. The system consists of interconnected computational nodes managed by a centralized or decentralized federation manager, depending on the deployment model. Each node contains specialized components that work together to process biological data while preserving privacy. These components include a local computational engine that handles data processing, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships by connecting various data sources, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaborate on complex biological analysis tasks without compromising their sensitive data, enabling breakthrough discoveries through shared computational resources and expertise while maintaining the security, compliance, and confidentiality required in biological research.

A federated distributed computational system enables secure, privacy-preserving biological data analysis and engineering through interconnected nodes coordinated in a distributed graph architecture. A federation manager allocates resources, manages data flow and lineage, establishes privacy boundaries, and maintains cross-institutional knowledge relationships. Each node contains a processing unit for biological data analysis, privacy preservation protocols for secure multi-party computation, a knowledge graph structure with supporting data stores, and encrypted network connections. The federation manager enforces all computation and data exchange through secure channels while maintaining privacy, security, and contractual boundaries. This architecture enables research institutions to collaborate on complex biological analyses without compromising sensitive data, facilitating breakthrough discoveries through shared computational resources while maintaining strict data privacy and security controls.

A federated distributed computational system enables secure collaboration across multiple institutions for multi-species biological data analysis. The system consists of interconnected computational nodes managed by a central federation manager. Each node contains specialized components that work together to process multi-species biological data while preserving privacy. These components include a local computational engine that handles data processing, a physics-information integration subsystem that combines physical state calculations with information-theoretic optimization, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities and manages resource allocations across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaboratively analyze complex, multi-species biological systems through integrated physics-based modeling and information-theoretic approaches while maintaining security and confidentiality.

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.

The invention discloses a livestock health state real-time monitoring method and system based on the Internet of Things, and the method comprises the steps: collecting livestock biological data and environment parameters through a biological chip ear tag and an environment sensor, and carrying out the preprocessing, and obtaining a standardized multi-dimensional livestock data flow; in combination with image data provided by a video monitoring system, identifying livestock individuals and analyzing behavior features, and fusing biological data and visual data to generate livestock individualized feature vectors; constructing an individualized health baseline model in combination with historical health records; comparing the deviation degree between the current state and the health baseline through an anomaly detection algorithm, and identifying a potential health problem; early warning is performed according to the abnormity severity level, intervention suggestions are generated, early warning information is pushed to the breeding personnel through multiple channels, and response measures and results are recorded to form closed-loop management. According to the invention, accurate real-time monitoring of the health state of the livestock is realized, the accuracy and timeliness of anomaly detection are improved, the breeding risk is reduced, and the production efficiency of animal husbandry is improved.

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.

A federated distributed computational system enables secure collaboration across institutions for unified biological and multiomics data analysis. It comprises interconnected computational nodes managed by a central federation manager. Each node includes specialized components: a local computational engine for biological data processing, a privacy-preservation system, a knowledge integration component leveraging dynamic knowledge graphs, and a secure communication interface. The federation manager coordinates computational activities while ensuring security, privacy, legality, and contractual adherence. This architecture allows institutions, citizen scientists, and patients to collaborate on complex biological analyses without compromising sensitive data. By enabling shared computational resources and expertise, the system facilitates breakthrough discoveries while maintaining confidentiality. Additionally, it supports pro-rata or contractually defined participation in resultant benefits or knowledge, ensuring equitable collaboration.

The invention relates to a seat self-adaptive adjusting system and method based on multi-modal biological data, and the system comprises a multi-modal data collection module, a data processing and fusion module, a control decision module, and an execution mechanism. Physiological and posture information of a user is collected through a heart rate sensor, a myoelectricity sensor, a skin temperature sensor, a pressure distribution sensor, a three-dimensional posture sensor and other multi-mode sensors, a fatigue index and a posture deviation amount are calculated through feature extraction, normalization and weighted fusion, and the fatigue index and the posture deviation amount are compared with preset threshold values. The control decision module generates adjusting instructions of the backrest angle, the waist support height, the cushion inclination angle and the temperature control power based on fuzzy control and a prediction algorithm, and self-adaptive adjustment of the seat is completed through an execution mechanism. The method can achieve the precise recognition and active intervention of the fatigue and posture change of the user, is suitable for various scenes such as driving, working and rehabilitation, can remarkably improve the comfort, delays the muscle fatigue, and improves the posture health.

The invention discloses an intelligent evaluation system and method for a mine restoration effect based on multi-source data, and relates to the technical field of data analysis. Environmental data, engineering data and biological data in original mine restoration are collected; performing space-time alignment on all the data cubes; performing weighted summation on the precision, the coverage rate and the freshness of each type of repair data to obtain a quality score, and constructing a quality scoring mechanism; calculating the dynamic weight of each type of repair data, constructing a score fusion model by using the knowledge graph correction coefficient and the dynamic weight of each type of repair data, collecting a historical mine repair score and a professional lowest requirement score, and calculating to obtain a quality score threshold system; judging the fusion score by using a quality score threshold system, and outputting a score report; according to a fusion score calculation process, setting an original data node, a data processing node and a conclusion node to construct a traceability graph; and outputting the traceability map while outputting the scoring report.

The invention relates to the technical field of marine organism protection, in particular to a marine organism diversity protection method based on an ecological dynamic model, which comprises the following steps: collecting biological and non-biological data in a marine ecosystem, extracting DNA from a seawater sample by utilizing an environmental DNA technology, and constructing the ecological dynamic model based on the collected ecological data and eDNA analysis result. According to the method, the ecological dynamics model is established, the collected data is stored and verified through the block chain technology, and a marine biological diversity protection scheme is designed and implemented according to the ecological dynamics model prediction result and the block chain verification data. Scientific support is provided for protection decision making, protection measures and management regulations are automatically executed through the intelligent contract technology, the system is allowed to respond immediately when it is detected that key environment variables or biological indexes change, and the flexibility and timeliness of responding to environment changes are greatly improved.

The invention is suitable for the technical field of group teaching, and particularly relates to a group teaching strategy generation method based on a smart learning cloud platform, and the method comprises the steps: obtaining the teaching behavior data, biological data and teaching progress data of a plurality of classes through the smart learning cloud platform; according to the teaching progress data of the plurality of classes, carrying out space-time alignment processing on the teaching stages of the plurality of classes to obtain a teaching stage sequence; based on each teaching stage of the teaching stage sequence, performing group anomaly detection on a plurality of classes to obtain an anomaly detection result; and generating a group teaching strategy based on the teaching behavior data and the biological data of the teaching stage in the anomaly detection result. According to the method, group problems can be found and corrected in time, and teachers are helped to make more accurate teaching decisions, so that the overall teaching quality and learning effect are improved.

The invention discloses a livestock feed proportioning method based on livestock growth, and relates to the technical field of feed proportioning, and the livestock feed proportioning method comprises the following steps: collecting the body temperature and motion track data and environmental temperature and humidity data of livestock individuals, monitoring the concentration of amino acids in digestive tracts, preprocessing the collected data, and generating a standardized biological data set; analyzing the compensation amount of vitamins and trace elements according to the metabolic fingerprint spectrum, and constructing a multi-objective optimization function in combination with an individual growth curve; solving the multi-objective optimization function through a quantum annealing algorithm, and matching local raw material inventory data of block chain evidence storage to generate an optimization strategy; and executing the optimization strategy and detecting the mixing uniformity, and when the mixing uniformity index exceeds a preset mixing uniformity threshold, starting the compensation device to adjust the optimization strategy. According to the invention, through a cooperation mechanism of dynamic nutritional requirement identification and multi-constraint optimization, the precise regulation and control capability of livestock breeding is significantly improved.

Embodiments are directed to a method for determining relationships between genes, phenotypes, and diseases that includes extracting a first set of biological data containing gene data, phenotype data, and disease data from a database, extracting a second set of biological data from a database, creating nodes on a network pertaining to each individual gene, phenotype, and disease data extracted from one of a database and documents, training multiple machine learning (ML) algorithms on a set of extracted data with matching empirical results, using at least one of the ML algorithms and the second set of biological data for determining relationships between the nodes, creating at least one of a gene-disease association score, gene-phenotype association score, and disease-phenotype association score for the relationships based on the relative association of the nodes, and displaying at least one of the association scores to a user.

The invention provides a wild animal and plant species identification method, which comprises the following steps: preprocessing collected biological data and environmental data associated with a target species to obtain to-be-analyzed data, the biological data comprising an image, an audio and positioning data associated with the target species, the environment data comprises meteorological and physical environment data associated with the living environment of the target species, and the target species are wild animals or wild plants; performing feature extraction on the to-be-analyzed data, and aligning the extracted multi-modal features based on time; based on the dynamically adjusted weight of each modal feature, performing feature fusion on the aligned multi-modal features to generate a comprehensive feature vector of the target species; and processing the comprehensive feature vector based on the target large model, obtaining at least one species name of the target species and the confidence of each species name, and determining a final name based on the confidence. According to the method, environment feature processing and dynamic weight adjustment mechanisms are introduced, so that species identification can be intelligently and accurately carried out.

The invention relates to the technical field of classified storage of biological data, in particular to a classified storage method and system for biological cell data. The method comprises the following steps: acquiring multisource cytomics data; performing data standardization on the multisource cytomics data to obtain standard cell characteristic data; performing characteristic matrix conversion on the standard cell characteristic data to obtain a cell characteristic digital matrix; performing molecular fingerprint construction on the biological cells based on the cell characteristic digital matrix to obtain cell molecular characteristic fingerprint data; performing hierarchical classification on the standard cell characteristic data to obtain a cell phenotype classification system; performing hierarchical labeling on the standard cell characteristic data according to the cell phenotype classification system to obtain cell phenotype labeling data; and performing ontology mapping on the cell phenotype labeling data to obtain a cell phenotype relationship network. According to the method, refined classification, dynamic updating and efficient storage of the biological cell data are realized.

The invention belongs to the technical field of bioinformatics, particularly relates to a computer simulation and regulation method and system for beef cattle fat metabolism, and aims to solve the problems of excessive fat deposition, low feed conversion efficiency, difficulty in multi-objective optimization and the like in existing beef cattle breeding. A multi-scale biological network model is constructed, a computer simulation and prediction engine is developed, an intelligent intervention strategy optimization engine is designed, and implementation and feedback optimization are carried out. By reducing unnecessary fat deposition, improving the feed conversion efficiency and improving the beef quality, the economic benefit of beef cattle breeding is remarkably improved, the requirements of consumers for high-quality and safe beef products are met, and the method has important value for promoting intelligent upgrading and sustainable development of the beef cattle industry.

The invention provides a terrestrial ecosystem service function monitoring and evaluation method, and the method comprises the steps: generating a biological feature vector and an environment feature vector according to biological data and environment data collected in an ecological protection region; performing species identification on the biological feature vector based on a biological large model, performing environment analysis on the environment feature vector based on an environment large model, and obtaining animal and plant information and environment quality evaluation conditions in the ecological protection area; and based on an ecological system evaluation model, carrying out comprehensive reasoning analysis on the organisms, the environment, the comprehensive feature vectors, animal and plant information in the ecological protection area and environment quality evaluation conditions, and evaluating an ecological system service function of the ecological protection area. Wherein the comprehensive feature vector is determined based on the fusion of the biological feature vector, the environment feature vector and the dynamically adjusted weight. According to the method, the overall ecological condition of the ecological protection area can be more comprehensively understood by adopting a large model technology, and more comprehensive, accurate, dynamic and scientific ecological system service function evaluation is provided.

The invention discloses a high-dimensional feature selection method of an evolutionary multitask optimization algorithm based on proxy assistance, and mainly relates to the field of feature selection and intelligent optimization. The invention designs a novel evolutionary multi-task optimization technical scheme for solving three defects of an existing evolutionary multi-task optimization algorithm for solving high-dimensional feature selection. In the task generation stage, a task generation strategy considering linear and nonlinear correlation at the same time is provided, and diversity is improved; in the knowledge migration stage, an agent-assisted knowledge migration strategy is provided for the farmer particles, and forward knowledge is migrated by evaluating aggregated knowledge and non-aggregated knowledge through agent assistance; in addition, a bidirectional asymmetric flipping strategy is provided for winner particles to improve the classification accuracy. By carrying out feature selection on a small-sample high-dimension biological data set, experiments show that compared with other feature selection methods based on an evolutionary multi-task optimization algorithm, the method can obtain a better feature subset.

The invention relates to the technical field of biomedical engineering, in particular to a digital twinning-based biological fermentation reaction kettle process control modeling method, which comprises the following steps: step 1, multi-source heterogeneous data acquisition and treatment: acquiring physical, chemical and biological data of a fermentation reaction kettle, and carrying out cleaning, complementing and time sequence alignment; step 2, mapping knowledge domain driven biological mechanism modeling: constructing a cell metabolism network mapping knowledge domain, associating process parameters with a biological mechanism, and optimizing a feeding strategy; step 3, performing digital twinborn driven multi-physical field simulation: constructing a CFD-molecular dynamics coupling model, and simulating a fermentation tank flow field and a cell metabolism process; 4, quality prediction and control of mechanism-data dual-mode driving are carried out; and 5, deploying and optimizing a cloud edge-end collaborative model. By constructing a'mechanism-data 'dual-drive architecture, the problems of insufficient adaptability, weak multi-modal data fusion capability and the like of a traditional model are solved.

The invention relates to the technical field of vehicles, in particular to a risk assessment method and device for a vehicle, the vehicle and a storage medium, and the method comprises the steps: obtaining biological data of a driver, state data of the current vehicle and environment data; inputting the biological data of the driver, the state data of the current vehicle and the environment data into a preset risk assessment model to obtain a comprehensive risk score; and determining a risk level of the current vehicle based on the comprehensive risk score and the duration to execute a response action according to the risk level. Therefore, the problems that the monitoring accuracy is reduced, the health data of the driver cannot be combined and emergency health events cannot be dealt with due to the fact that the risk assessment of the vehicle is easily influenced by external factors are solved, the driving risk can be comprehensively assessed in combination with the biological data of the driver, the vehicle state data and the environment data, and the risk assessment precision is improved.

The invention belongs to the technical field of biological data processing, and discloses a multi-omics data fusion method and system based on a hypergraph network. According to the method, a hypergraph structure is adopted for modeling omics data, high-order interaction information can be more efficiently mined, and a complex association mode between biological entities can be more comprehensively revealed; by introducing a hypergraph aggregation mechanism, high-order complex relationships in various omics data can be represented more accurately, the limitation that a traditional method can only capture the relationship between every two nodes is overcome, and the understanding ability of the model to the interaction between biological entities is improved; according to the hypergraph fusion method, multi-omics specific hypergraphs are effectively integrated, unified representation of cross-omics data is realized, the representation capability of the model for complex multi-relational data is enhanced, and more comprehensive technical support is provided for multiple downstream tasks.

The invention discloses a drug-disease interaction prediction method and device, a medium and a product, and relates to the field of intelligent medical treatment. Firstly, drug data, disease data and biological data are acquired; carrying out preprocessing and feature extraction on the drug data to obtain drug molecular features; extracting target spot features based on the biological data; inputting the drug molecular features and the target features into a first full-connection neural network for predicting a drug-target interaction relationship; generating drug biological network characteristics based on the drug-target interaction relationship; performing preprocessing and feature extraction on the disease data to obtain disease features; training a second full-connection neural network based on the drug-biological network features and the disease features, and obtaining a drug-disease interaction prediction model after training is completed; the drug-disease interaction is predicted by using the drug-disease interaction prediction model, and the prediction efficiency and accuracy can be improved.

Systems, devices, and methods for processing, analyzing, and classifying biological data sets and generation of cell profiles are provided herein. The data set may include multiple omics data. Some embodiments may include using machine learning to train classifiers of raw multi-omics data and incorporate systematic biological knowledge to understand cellular behavior and cellular status at biomolecular levels.

Techniques, systems and devices for glycemic control are presented. An apparatus may include a display device and a processor in electronic communication with the display device. The apparatus includes a memory communicatively connected to the processor. The memory includes instructions configuring the processor to generate a user interface through the display device and receive user input through the user interface. The processor is configured to implement an activity mode of a plurality of activity modes of a wearable medical device based on the user input. The activity mode is indicative of temporary conditions affecting blood glucose levels of the user. The processor is configured to receive biological data from a user through a biological sensor in communication with the processor and calculate an amount of medication to deliver to a user based on the biological data and the implemented activity mode.

The invention provides a braking control method and a vehicle, and relates to the technical field of vehicle braking. The method comprises the following steps: acquiring braking parameters and driving parameters of a vehicle and biological data of a driver at the current moment; determining a driving style, a driving intention and a driving state of the driver at the current moment according to the driving parameters and the biological data; wherein the driving style is used for representing the preference of the driving behavior of the driver, the driving intention is used for representing the driving behavior of the driver in a preset scene, and the driving state is used for representing the physiological and psychological states of the driver; according to the driving style, the driving intention and the driving state, a braking control instruction corresponding to the braking parameters is determined; according to the technical scheme, braking control is conducted on the vehicle on the basis of the braking control instruction, braking control can be effectively adjusted according to the actual driving condition, and therefore the actual braking effect adapts to the driving behavior of a driver, the driving comfort is improved, and the driving safety is improved.

The invention belongs to the technical field of tumor clustering analysis, and particularly relates to a tumor clustering analysis system and method based on big data. Through deep fusion and space-time alignment processing of multi-dimensional biological data, the limitation of single-dimension or simple data integration in a traditional method is overcome, complex biological characteristics and space-time evolution laws in the tumor can be captured more comprehensively, and the tumor can be rapidly and accurately captured by constructing a low-dimensional association and independent characteristic space and combining a nonlinear dimension reduction technology. According to the method, low-dimensional feature vectors with biomarker features are effectively extracted, a high-quality data basis is provided for subsequent clustering analysis, meanwhile, based on sliding window density estimation and determination of a dynamic neighborhood radius, accurate spatial adjacency relation construction of the low-dimensional feature vectors is achieved, and the accuracy of the spatial adjacency relation is improved. And the potential clustering center of the tumor subtype is extracted through the multi-scale characteristic spectrum, so that the generated classification label can more accurately reflect the real distribution of the tumor subtype.

The invention relates to a method for predicting the lifespan of an individual, comprising I. providing biological input data of the subject, ii. providing a list of confounding variables, iii. predicting an age-related mortality or disease, preferably the time to death and / or the mortality risk and / or the risk of age-related disease, for the subject by analyzing the data with an algorithm, wherein the algorithm comprises a neural network, which is trained on at least one reference dataset comprising biological data of at least one reference subject by applying: a) a selector layer to filter input data of i., and b) an adversarial learning framework, that removes from the input data the information related to the confounding variables, wherein preferably the adversarial learning framework, is a neural network comprising three elements: a feature extractor (FE), a predictor (P), and a confounder predictor (C). The invention further relates to a device or system comprising means for carrying out the steps of the method according to the invention, a computer program and a computer-readable storage medium.

The invention discloses an intelligent pre-warning system for Parkinson / epilepsy recognition based on a multi-modal sensor and a bracelet, and relates to the technical field of biosensors. The intelligent early warning system for Parkinson / epilepsy recognition based on the multi-modal sensor comprises a multi-modal acquisition interference judgment module, a multi-modal interference processing optimization module and a multi-modal fusion input early warning module. According to the method, whether the multi-modal interference processing optimization is carried out or not is judged according to the multi-modal interference early warning result, if yes, the multi-modal fusion instruction is sent after the multi-modal interference processing optimization, and if not, multi-modal biological data fusion is directly executed, and the multi-modal fusion early warning result is obtained. Finally, whether multi-modal fusion input early warning is carried out or not is judged based on the multi-modal fusion early warning result, the effect of carrying out physical condition early warning more accurately is achieved, and the problem that in the prior art, in the process of carrying out physical condition early warning through wearable equipment, multi-modal biological data collection and fusion association are not sufficient is solved.

The invention provides a defect early warning method and device for multi-source heterogeneous data fusion of power equipment, and the method comprises the steps: collecting biological data, quantum data and operation data of the power equipment; performing biomolecular diagnosis on the biological data to obtain biological characteristic information; laying a quantum cobweb based on the quantum data to obtain quantum feature information; performing feature fusion on the biological feature information and the quantum feature information to obtain feature fusion information; performing space-time coding conversion on the feature fusion information and extracting bionic features to obtain space-time feature information; generating a space-time pulse characteristic flow based on the space-time characteristic information; constructing a holographic decision field through the space-time pulse characteristic flow; performing risk deduction by using the holographic decision field to generate a defect evolution cloud picture; and generating defect early warning information based on the defect evolution cloud atlas and the operation data. According to the method, potential defects of the equipment can be accurately found in advance, the equipment failure rate is reduced, and the reliability and stability of the power system are improved.