151 results about "Biological data" patented technology

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.

Provided herein are systems, devices, and methods for processing, analyzing, and classifying biological data sets and generation of cell profiles. The data sets may include multi-omic data. Some embodiments may include the use of machine learning in training a classifier of raw multi-omic data and incorporating system biology knowledge to understand cellular behavior and cell status at the biomolecular level.

The present application relates to the technical field of emotion recognition, in particular to an asynchronous multi-modal emotion recognition method, device, equipment and medium. The present application respectively carries out pulse coding on each modality biological data of the user, obtains the pulse sequence of each modality biological data, applies cross-modal synchronous neurons to each pulse sequence based on the firing time of pulse coding, obtains the emotion feature of the user, and finally applies an emotion classifier to the emotion feature to obtain the emotion classification result. Since the pulse sequence formed by the pulse coding of the present application can record the time when the biological data changes, and the cross-modal synchronous neurons are applied to the pulse sequence according to the firing time of the pulse coding to analyze the emotion feature, the combination of the pulse coding and the cross-modal synchronous neurons of the present application can identify the time when each multi-modal biological data changes, and identify the emotion feature of the user according to the time sequence before and after the change of the multi-modal biological data, so as to improve the accuracy of emotion recognition.

The invention relates to an N-BEATSx plankton prediction method fusing a semantic model. The method comprises the following steps: acquiring an observation data set in a current environment; performing multi-scale interpolation processing on the observation data set according to interpolation strategies corresponding to different interval durations to obtain a target observation data set comprising target multi-source environment data and target plankton data; and in the improved N-BEATSx network fused with the semantic model, performing cross attention operation and residual operation on the target multi-source environment data and the target plankton data to obtain a prediction result based on the plankton variation in the current environment. By adopting the method, semantic fusion and dynamic prediction of the environmental data and the biological data can be realized.

The invention provides a diabetes development stage identification method and device based on a bio-electricity signal, and relates to the technical field of biological data analysis, and the method comprises the steps: obtaining a bio-electricity signal triple set and diabetes data, the bio-electricity signal triple set comprises a plurality of bio-electricity signal triads, and the diabetes data comprises a diabetes development stage text; obtaining a mapping relation between the bioelectricity signal triple and the diabetes development stage text, and labeling the diabetes development stage text by taking the mapping relation as a label; performing iterative training on the initial diabetes development stage identification model through the bioelectricity signal triple set and the labeled diabetes development stage text set, and obtaining a final diabetes development stage identification model after training is completed; and inputting the to-be-measured bioelectricity signal triple into the final diabetes development stage identification model to carry out diabetes development stage identification so as to obtain a final prediction result of the diabetes development stage.

A personalized supplement formulation system integrates biological data, structured qualitative feedback, and machine learning algorithms to generate individualized nutritional protocols. The system receives biological inputs such as genetic single nucleotide polymorphism (SNP) data, blood biomarkers (e.g., ferritin, vitamin D, B12), lipid profiles, and urinary metabolites. A digital user profile is created by organizing these inputs and calculating derived indices relevant to supplementation. Structured qualitative feedback, including dietary restrictions, perceived wellness, and training goals, is normalized and processed alongside the biological data. A trained machine learning engine analyzes combined inputs to output a tailored supplement formulation specifying ingredient selection, dosage, delivery format, and timing. Instructions are transmitted to a manufacturing system capable of producing the custom formulation. The system supports periodic re-evaluation and iteration based on new biological samples or user-reported feedback, enabling dynamic personalization over time and improving efficacy, compliance, and outcome tracking in athletic and wellness domains.

The application discloses a cross-industry data assetization management and circulation system, which comprises five core modules. A data block encryption module receives cross-industry biological data flow, performs sequence block and nested encryption, and generates an encrypted data flow; a blockchain section storage module generates a hash storage certificate for the data block and writes the hash storage certificate into a blockchain; a federal contribution attenuation evaluation module receives a federal learning gradient update signal, performs time attenuation and marginal benefit adjustment to generate a contribution evaluation signal; a cross-modal knowledge graph alignment desensitization module realizes cross-modal entity semantic alignment, calls a configurable desensitization rule chain to generate a desensitized data flow; and a data circulation gateway module integrates three types of signals to generate a data asset circulation control signal. The application solves the collaborative management problems of safe storage, privacy protection, contribution quantification and compliance desensitization in the circulation of cross-industry biological data.

The invention relates to a medical beauty implant material intelligent optimization method and system based on artificial intelligence, and the method comprises the following steps: S1, obtaining the performance parameters of different medical beauty implant materials, capturing the anatomical features of a patient through 3D scanning and MRI images, and carrying out the statistics of the personalized biological data of the patient; s2, constructing a multi-task learning model and training based on a multi-target supervised learning data set, wherein the target is to predict the performance of different implant material formulas in a medical beauty implant environment; s3, performing multi-objective optimization by adopting an NSGA-III algorithm to obtain an optimal implant material formula; s4, reconstructing a three-dimensional model of the patient by using a reverse modeling technology based on anatomical features of the patient; and S5, based on the three-dimensional model, according to the optimal implant material formula and the personalized biological data, simulating the biomechanical characteristics of the patient by utilizing finite element analysis, and outputting biocompatibility scores and suitability suggestions of the material in different implant environments. According to the method, the clinical requirements on customized design and application of the medical beauty implant material are effectively met.

The present application relates to computer vision and underwater detection technology, provide a kind of underwater small biological target detection method and system based on deep learning, the WLDA-YOLOv8 deep learning model constructed includes main network, neck network and detection head, main and neck embed light multi-scale module, detection head introduces dynamic detection head and adaptive sample selection strategy;Training stage uses improved WIoUv3s loss to carry out boundary box regression, the model is trained using underwater biological data set, finally realizes the accurate detection of small target in underwater image, enhances the capture and fusion ability to small target subtle features.

The invention provides a neural feedback training scheme automatic screening and effect suggestion method. The method comprises the following steps: acquiring biological data of a testee by using a brain wave collection device; wherein the brain wave collecting device comprises a household brain wave collecting device; transmitting the biological data to a brainwave database of a remote cloud system through a network; the brain wave database converts the biological data into a corresponding training parameter suggestion; and remotely feeding back the training parameter suggestions to a neural feedback training module for the testee to perform heart and brain training. Therefore, suggested training parameters are provided through a software interface, and superior and inferior brain area networks, digital therapy suggested training schemes and related health education are arranged, so that the effects of long-distance feedback and improvement of cognitive competence by brain training at home are achieved.

Provided is a low-noise infusion pump and an automated infusion system using the same, including: a wearable infusion bag device configured to be worn on an animal and to inject a fluid into a body of the animal, the wearable infusion bag device including a low-noise infusion pump operated by a low-noise driving motor; an in-body insertion sensing unit inserted into the body and configured to collect biological data; and an infusion control unit configured to control operation of the low-noise infusion pump based on operation data of the low-noise infusion pump and the biological data received from the low-noise infusion pump and the in-body insertion sensing unit via wireless communication.

An object of a system according to an embodiment is to provide a sleep improvement plan optimized for an individual user.SOLUTION: A system includes a biological data collection part, a life habit data collection part, an environmental factor data collection part, an analysis part, and a plan creation part. The biological data collection unit collects biological data. The lifestyle habit data collection unit collects lifestyle habit data. The environmental factor data collection unit collects environmental factor data. The analysis unit analyzes the biological data, the lifestyle data, and the environmental factor data. The plan creation unit creates a personalized sleep improvement plan based on the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

The present invention relates to a computer-implemented method and device for predicting a patient response to an agent, comprising receiving an avatar of the patient and respective data of organoid response for said patient, said avatar comprising biological data and clinical data of said patient, said avatar and said respective data of organoid response defining at least one patient feature and at least one organoid response feature respectively; receiving a first ensemble of one or more trained learning models; calculating the first output vector by providing one or more of said at least one patient feature and one or more of at least one organoid response feature as input to said first ensemble of one or more trained learning models; obtaining at least a PFS and / or an OS for the patient using the first output vector; and obtaining a prediction of the patient response to the agent using said obtained PFS and / or OS.

An object of a system according to an embodiment is to analyze biological data of participants and automatically organize an effective breakout room.SOLUTION: A system includes a biological data collection section, an analysis section, a classification section, and an organization section. The biological data collection unit collects biological data such as facial expressions and voice quality from a camera or a microphone of a terminal of a participant. The analysis section analyzes the biological data collected by the biological data collection section. The classification component classifies the participants by character on the basis of the data analyzed by the analysis component. The organization unit automatically organizes a breakout room based on the characters classified by the classification unit.SELECTED DRAWING: Figure 1

A system for determining heart failure exacerbation includes a wearable device measuring biological data and motion data of a subject, and a server connectable to the wearable device to: acquire the biological data and motion data at each time point in a predetermined period; calculate a parameter related to a heart based on the biological data; calculate an activity amount based on the motion data; identify active time points where the calculated activity amounts are equal to or greater than a predetermined value; identify specific parameters at delayed time points when a predetermined time has elapsed from the active time points; and calculate an index value related to the heart failure exacerbation based on a relationship between the activity amounts at the active time points and the specific parameters at the delayed time points.

The utility model discloses convenient biological data storage equipment, which relates to the technical field of data storage equipment and comprises a shell, a protective cover is arranged on the outer side of the shell, and a storage hard disk is arranged in the shell. A linkage rod is connected to the inner side of the shell in a nested mode, a limiting plate is fixedly connected to the outer end of the linkage rod, a clamping mechanism capable of driving the limiting plate to move is arranged between the inner end of the linkage rod and the interior of the shell, a front butt joint opening is formed in the front side of the shell, and a rear butt joint opening is formed in the rear side of the shell. According to the convenient biological data storage equipment, after a butt joint rod is in butt joint with the interior of a butt joint hole, the butt joint rod can push a push plate to move in the butt joint hole, at the moment, an inclined groove formed in the lower side of the push plate can abut against a linkage column, and therefore the linkage column moves along the side of the inclined groove; and a limiting plate is further pushed to move through a linkage rod, so that the side of the storage hard disk is clamped and limited.

This invention provides a method and system for biodiversity analysis combining neural networks and knowledge graphs in the field of biodiversity analysis technology. The method includes: Step S1, creating a biological knowledge extraction model; Step S2, acquiring a large amount of historical biological data to construct a dataset and training the biological knowledge extraction model; Step S3, extracting named entities, entity relationships, and multimodal information from the latest biological data using the biological knowledge extraction model to construct structured triples; Step S4, defining an ecological ontology and converting the ecological ontology and structured triples into knowledge representations; Step S5, constructing a knowledge graph based on the knowledge representations and defining logical rules for biodiversity analysis; Step S6, obtaining biodiversity analysis results through analysis and reasoning using the knowledge graph and logical rules. The advantage of this invention is that it greatly improves the accuracy of biodiversity analysis.

The application provides a product retail advertisement pushing method and system, electronic equipment and storage medium, and relates to the technical field of intelligent advertisement pushing and consumer behavior analysis. The method comprises the following steps: collecting user biological data of a target product retail display area; performing multi-dimensional processing on the user biological data to generate a feature change mode set, a pupil response sequence and a physiological response curve; performing multi-modal data fusion on the feature change mode set, the pupil response sequence and the physiological response curve, combining an emotional computing algorithm to determine the emotional feedback intensity value of the user to the current advertisement content; generating a personalized advertisement scheme matched with the emotional state of the user according to the emotional feedback intensity value and combining a generative adversarial network; matching the personalized advertisement scheme with product characteristic data browsed by the user to generate specific advertisement content, and pushing the specific advertisement content to the user. The application improves the real-time accuracy and user emotional matching degree of advertisement pushing.

The application is suitable for the technical field of bioinformatics, and provides a phosphorylation site and disease association prediction method based on a graph neural network, comprising the following steps: constructing a multi-view of phosphorylation sites and diseases based on multi-source biological data; constructing a multi-view heterogeneous graph based on a constructed phosphorylation site sequence similarity matrix and a disease semantic similarity matrix combined with site-disease association information; encoding the constructed view heterogeneous graph through a graph layer attention mechanism to generate a site and disease representation; and optimizing the site and disease representation using a contrast learning method and performing prediction. The application significantly improves prediction accuracy, solves problems such as insufficient information utilization, weak feature expression capability and over-smoothing of deep graph neural networks in existing methods.

This invention relates to the field of biological data processing technology, and in particular to an artificial intelligence-based intelligent fetal heart rate monitoring and risk warning system. The system collects multidimensional fetal heart rate information and performs noise reduction and quality assessment on this information using a data quality evaluation module. It also constructs a spatial-temporal heart rate feature fusion model using an intelligent heart rate monitoring module, obtains a medical advice level based on the fetal heart rate feature vector using a heart rate risk warning module, and provides tiered medical advice warnings. Furthermore, the system updates the collection frequency based on the heart rate pattern status using a heart rate monitoring calibration module, thereby improving the accuracy and stability of fetal heart rate monitoring at the data level.

A computational system and method for digital modeling of biochemical reactions within cardiac cells are disclosed. The system comprises processors and a computer-readable storage device with instructions to maintain biological data related to cardiac cell reactions and receive user selections via a user interface to configure a simulation. The user can select a cardiac cell class, and functions to load cellular components, generate enzyme activity, load specific enzymes, and perform enzymatic reactions. The system performs in silico experiments, predicts new biological data, and compiles this into configuration data. An enzyme activity model is generated and simulated under various conditions, including user-defined enzymatic dysregulation, to replicate and study cardiac physiology and pathology. The results, such as changes in energy metabolism parameters like glucose consumption and ATP usage, are outputted. The system provides a detailed and interactive platform for research and education in cardiac electrophysiology and disease.

The invention discloses a synthetic biological data intelligent extraction system based on a multi-modal large language model, and belongs to the technical field of biological data intelligent extraction, and the system comprises a multi-modal data preprocessing and standardization module which is used for carrying out the analysis, classification and structured output of input heterogeneous synthetic biological data; and the multi-modal large language model processing module is connected with the preprocessing module and is used for receiving the processed structured data and carrying out joint understanding and information extraction through knowledge enhancement and deep fusion technologies. The heterogeneous synthetic biological data can be efficiently processed, different types of data such as scientific literatures, chart images and biological sequences are accurately analyzed and classified through the multi-modal data preprocessing and standardization module, the data format barrier is broken, scattered biological information is systematically integrated, and the data processing efficiency is improved. A standard and unified data foundation is laid for subsequent information extraction, and the comprehensiveness and effectiveness of data processing are greatly improved.

The invention provides a B cell lymphoma prognostic risk layered prediction method and device, and relates to the technical field of biological information. The prediction method comprises the following steps: acquiring clinical examination data and BCR immune repertoire sequencing data of a to-be-predicted object; extracting a prognosis key feature set, inputting the prognosis key feature set into the risk hierarchical model, and performing clustering analysis on feature distribution of the to-be-predicted object; and determining a prognosis risk layering level of the to-be-predicted object according to an output result of the clustering analysis. According to the method, multi-dimensional key features of clinical and BCR immune repertoire are fused, and the Gaussian mixture model is utilized to perform probability clustering on complex biological data, so that the defect of insufficient discrimination of traditional hard stratification is effectively overcome, different risk subgroups are accurately identified, and the accuracy and clinical guidance value of prognosis evaluation of B-cell lymphoma are remarkably improved.

The application discloses a diabetes patient biological age calculation method based on glycosylation markers and a multi-task deep learning framework, and belongs to the technical field of bioinformatics and medical data processing technology, and the method comprises the following steps: obtaining glycomics feature data and clinical examination data of diabetes patients; preprocessing the data; obtaining a feature set by adopting a multi-method screening and fusion strategy; constructing a deep learning model comprising a multi-modal feature fusion module and a multi-task learning module for training, wherein the multi-task learning module comprises a main task of aging clock regression and at least one auxiliary task; and outputting biological age prediction values and age acceleration evaluation results of individuals by using the trained model. The method realizes deep fusion of multi-modal biological data and exclusive modeling for the diabetes population, can accurately quantify the biological aging process and the deviation degree relative to the calendar age, and provides an objective quantitative tool for clinical evaluation.

The present disclosure provides an estimation device, a learning model, an estimation method, a learning model generation method, a biological data processing system, a biological data processing method, and a computer program. The estimation device estimates biologically relevant state information of a subject. The estimation device is provided with: an estimation unit that inputs input information to a learning model and acquires output information from the learning model; and a storage unit that stores the output information. The output information includes the biologically relevant state information. The biologically relevant state information includes information representing the mental and physical states of the subject or information representing an environmental state that may affect the mental and physical states of the subject. The input information includes at least cumulative data calculated by accumulation processing performed on a plurality of pieces of biological data of the subject which are arranged in time series. The accumulation processing indicates processing of accumulating the biological data under the condition in that a value does not diverge due to the accumulation.

To improve prediction accuracy when there are gaps in time-series biological data. [Solution] The analysis device includes an input unit that inputs first time-series data of detected values ​​of a living organism and second time-series data indicating the state of the living organism; an extraction unit that extracts time-series detected values ​​from the first time-series data for a specific state period; a first aggregation unit that aggregates the time-series detected values ​​for that period unit by unit time and outputs first one-dimensional array data of statistical values ​​time-series; a second aggregation unit that aggregates missing detected values ​​in the time-series data of detected values ​​unit by unit time and outputs second one-dimensional array data of missing features time-series; a splitting unit that divides the first one-dimensional array data based on a specific frequency to generate a plurality of first two-dimensional array data, and divides the second one-dimensional array data to generate a plurality of second two-dimensional array data; a merging unit that combines the plurality of first two-dimensional array data and second two-dimensional array data; and an identification unit that performs a convolution operation on the combined result and outputs an identification result.

The application belongs to the technical field of biological information calculation, and discloses a kind of pet feed additive formula computing model optimization system, comprising: data interface obtains raw material parameter and contains environmental stress factor data;Memory stores heterogeneous neural network model, which contains first time series convolution subnet, second cascade processing subnet and global gating distribution unit coupled by lateral inhibition channel and is arranged in parallel;Processor runs model, utilizes global gating distribution unit to dynamically adjust the computing power weight between subnet according to system energy state tensor, and carries out back propagation iteration based on nonlinear constraint penalty term, the special computing architecture of sparse topological mapping and dynamic gating mechanism is constructed, the convergence difficulty and negative transfer problem when general model processes high-dimensional strongly coupled biological data are solved, and the prediction accuracy and safety of formula optimization are improved.

The invention relates to an identity authentication method, an identity authentication device and an identity authentication system. An identity authentication method according to an embodiment of the present disclosure may comprise: a step of transmitting a state update request signal to a server in response to receiving an authentication signal to trigger acquisition of authentication means biological data; in response to receiving the message related to the acquisition of the authentication means biodata, downloading the authentication means biodata from the server; and comparing the downloaded authentication means biometric data with the registered biometric data.

The invention discloses a smart watch dial personalized configuration method and system, and relates to the technical field of personalized smart wearable, and the method comprises the steps: collecting biological characteristic data of a user in a resting state through a smart watch, generating a user personalized tactile feedback configuration, and generating a user biological reference file; synchronously guiding the user to watch a dial plate to collect user watching behavior data, and generating a user personalized watching area prediction model; collecting user eye movement data in real time, extracting sight behavior characteristics, inputting the sight behavior characteristics into a user personality fixation area prediction model, judging a target fixation area, performing visual optimization on focus and non-focus areas by adopting visual enhancement, collecting real-time biological data through a biosensor, extracting user characteristics, and calculating a dynamic adjustment coefficient based on the biological characteristics; and synchronously obtaining user task data to optimize the dynamic adjustment coefficient, and adaptively matching and updating the personalized tactile feedback configuration of the user. According to the invention, matching and optimization of specific requirements of the user are realized, and the individuation degree of the intelligent equipment is improved.