162 results about "Informatics" patented technology

The control system (3000) has an input interface (311) that accepts input of input information (D31) from a user (1) as a command to a target device (2). The input information (D31) is information indicating a command composed of a plurality of operations to the target device (2). The learning model unit (300) inputs the input information (D31) to a learning model and outputs operation instructions (D32) indicating sequential control for realizing a series of controls in the target device (2) for implementing the plurality of operations.

The system according to this embodiment aims to enable users to practice effective proposal methods and negotiation strategies in advance, based on the characteristics of their clients' representatives. [Solution] The system according to the embodiment comprises a reception unit, a learning unit, a generation unit, a simulation unit, and a feedback unit. The reception unit inputs information of the contact person of the business partner. The learning unit learns based on the information input by the reception unit. The generation unit generates advice and suggestions based on the data learned by the learning unit. The simulation unit performs a dialogue simulation based on the advice and suggestions generated by the generation unit. The feedback unit inputs feedback on the dialogue performed by the simulation unit.

This invention relates to the field of bioinformatics, and particularly to a method and system for denoising single-cell immune repertoire sequencing data. The method includes data preprocessing and feature extraction, bidirectional collaborative denoising, intelligent comprehensive judgment and classification, data archiving and background learning, and result output. Compared to existing technologies that primarily rely on static thresholds for cell filtering, which struggle to comprehensively assess and eliminate multi-dimensional noise, leading to incomplete purification and the potential deletion of high-value cell information, this invention employs a systematic denoising scheme integrating multi-parameter dynamic threshold filtering, specific gene contamination analysis, and targeted optimization of VDJ data. It sets dynamic thresholds by integrating multi-dimensional quality control indicators such as UMI number, gene number, and the proportion of mitochondrial and ribosomal genes, and specifically identifies and filters interfering genes and background sequences. This enables refined and hierarchical removal of complex noise, significantly improving the overall quality of cell datasets and the accuracy of VDJ receptor sequence analysis.

The application relates to the technical field of medical care information science, and discloses an old-age health prediction method and system, which comprises an information collection module, a phase-locked analysis module and a trend determination module, the information collection module synchronously acquires a behavior perturbation sequence and a physiological response sequence; the phase-locked analysis module calculates dynamic phase offset characteristics between the behavior perturbation sequence and the physiological response sequence, and determines a logical deviation residual of a measured phase offset relative to a reference mapping track by using a logic transfer check unit; and the trend determination module monitors a second-order cumulative change rate of the logical deviation residual in a sliding window, and outputs a warning signal when the second-order cumulative change rate exceeds a stability threshold value; the application can perceive the deterioration of body feedback regulation flexibility before a physiological parameter appears abnormal, and avoids prediction failure caused by a physiological compensation mechanism of an old individual by quantifying the information transmission efficiency of a feedback loop.

A method for auto-correcting information extracted by an open information extraction system includes inputting an input triple extracted from an input sentence to a grammaticality model trained to output whether the input triple is grammatical or ungrammatical. The input triple is corrected based on the grammaticality model outputting that the input triple is ungrammatical. Embodiments of the present invention can be used in a variety of machine learning and artificial intelligence applications, including but not limited to several anticipated use cases in material informatics, data security, data extraction, and medical / healthcare, for example, for supporting decision making or optimizing and calibrating of extracted chemical compounds from libraries of textbooks and publications.

The application discloses an oral ulcer image recognition autoimmune disease blood index correlation analysis system and relates to the technical field of medical informatics. The system comprises a time series data acquisition module, a steady-state baseline construction module, a cross-modal synchronous analysis module, a deviation calculation module, a disease risk determination module and a risk warning output module. The system acquires the time series data of the oral ulcer image and the autoimmune related blood index of the user, and constructs an individualized immune steady-state baseline. The dynamic evolution rate of the image lesion is used as a guide factor to time extrapolate the non-synchronous collected blood index, generate synchronous predictive immune data, calculate the deviation of the predictive data and the steady-state baseline, and realize accurate and real-time determination and early warning of the autoimmune disease risk. The application effectively solves the evaluation delay problem caused by the asynchronous collection of the oral ulcer image and the blood index, and improves the timeliness and accuracy of health risk monitoring.

The application belongs to the field of medical prediction, and discloses an information mining method for heterogeneous time series data, comprising: acquiring electronic medical record data and constructing a hypergraph, analyzing and calculating the hypergraph to obtain embedding representation data, weighting the embedding representation data based on an attention mechanism to obtain embedding sequence data, constructing a sequence learning model and performing hidden state access to obtain hidden representation data and weight data thereof, weighting the embedding sequence data to obtain embedding sequence hidden data; training the sequence learning model through time training parameter data, weighting the embedding sequence hidden data through the trained sequence learning model to obtain time dimension hidden data, constructing a full connection network to analyze the time dimension hidden data to obtain medical event prediction data. The technical scheme disclosed by the application can learn complex information in the time dimension by using time step information, and can obtain accurate medical event prediction results.

This invention relates to the field of bioinformatics, and particularly to an artificial intelligence-based drug relocation method, apparatus, device, and storage medium. The AI-based drug relocation method of this invention first constructs a heterogeneous network, and then builds a relocation model including a multi-relational heterogeneous graph encoder, a multi-head attention mechanism module, and an interactive decoder. Based on the heterogeneous network, it predicts the treatment score of a drug for a disease, achieving better test metrics compared to other methods. Furthermore, this invention interprets the prediction results, identifying key functional nodes and explaining the pharmacological mechanism of drug treatment for diseases.

The present application relates to the technical field of bioinformatics and artificial intelligence, in particular to a virtual cell construction method and system, the method comprising: taking single cell gene expression matrix and perturbation condition data as input data; constructing an encoding network based on a structural causal model to obtain latent representation, and constructing a perturbation variable according to the perturbation condition data, modeling the latent representation and the perturbation variable to obtain decoupled latent representation; constructing a continuous time evolution path from an initial distribution to a target distribution based on a flow matching model, and determining state changes according to the decoupled latent representation; numerically solving the continuous time evolution path to output virtual cell expression data. The present application is used to solve the problems of causal aliasing, insufficient distribution out-of-distribution generalization ability, unstable generation process and difficulty in counterfactual reasoning in the existing single cell perturbation prediction method.

This invention discloses an intelligent optimization method for communication chip design parameters based on deep learning, comprising: S1, constructing chip topology graph data and encoding a graph structure input using Kirchhoff's laws; S2, collecting historical data to construct a standardized training sample library; S3, aggregating node information to learn parameter coupling relationships and outputting a performance prediction model; S4, introducing an improved QPSO algorithm to construct a physically constrained pseudo-gradient guiding term, using the first derivative to force particle movement in flat regions, and outputting a potential global optimal solution region; S5, using the MOEA / D algorithm to decompose a multi-objective problem and iteratively selecting non-dominated solution sets as candidate sets in parallel; S6, backfeeding a high-precision simulator for verification and incremental updates, outputting the optimal parameter combination. This invention achieves physical constraint modeling and gradient-guided optimization of design parameters, effectively improving optimization efficiency, prediction accuracy, and design feasibility.

The application relates to the technical field of medical informatics and intelligent treatment control, and discloses an electroencephalogram synchronous electroacupuncture stimulation calibration method and system based on embedded AI. The embedded AI module is used for collecting and intelligently analyzing brain electrical signals of a patient in real time, generating brain state characteristics, dynamically adjusting electroacupuncture stimulation parameters in combination with subjective feedback of the patient, and forming a closed-loop feedback system. The system carries out individualized baseline calibration before treatment, improves the accuracy of brain state recognition, continuously monitors the brain electrical signals during treatment, dynamically updates the brain state characteristics, adjusts the electroacupuncture parameters according to the brain state characteristics, and ensures the individualization and dynamic optimization of the treatment. The system also has long-term learning ability, continuously accumulates treatment experience, optimizes the association rules, and improves the accuracy and efficiency of the treatment. The application significantly improves the pertinence, response speed and stimulation calibration accuracy of electroacupuncture treatment, and provides a new solution for individualized medical treatment.

The application discloses a reaction optimization method based on double-view contrast learning and double-layer attention, and relates to the technical field of chemical informatics and artificial intelligence. The method comprises the following steps: constructing a double-view molecular representation model, extracting molecular features from a graph structure view and a path sequence view, and pre-training the model through self-supervised contrast learning; constructing a chemical reaction prediction model; pre-training the chemical reaction prediction model by using the extracted molecular features in a chemical reaction data set; taking the reaction prediction model as a proxy model of Bayesian optimization; and realizing the collaborative and rapid optimization of material variables such as reactants, catalysts and solvents, and numerical condition variables such as temperature and concentration. The application effectively solves the problems of missing molecular representation information and low optimization efficiency of high-dimensional mixed variables in the prior art, and significantly speeds up the chemical research and development process.

This invention provides a Slurm-based bioinformatics workflow management system. The module management subsystem stores bioinformatics software commands in a database according to a predetermined format, defining them as modules. These modules can be combined into new modules through serialization or network connections, enabling the rapid construction of complex workflows. The task submission system converts these modules into corresponding task scripts. These scripts are generated in a nested manner, consisting of a main job, sub-jobs, and the smallest job unit. The scripts conform to the Slurm job scheduling system specifications and include a progress report. This workflow management system, combined with front-end and back-end technologies, enables visual interaction, improves the management and utilization efficiency of bioinformatics software commands, and promotes the rapid development of life sciences.

The application discloses a kind of perfect preset area history flood disaster database method and system, belong to flood disaster information science and big data technology field;Including the following steps: obtaining preset area history hourly precipitation grid data, generates hourly grade grid map according to rainstorm threshold classification, connects the rainstorm area of corresponding moment of adjacent same grade grid;Combined with the process of spatial and temporal splitting and merging, complete rainstorm process is aggregated;Matching the corresponding city of preset area rainstorm flood related social media data, associate rainstorm process with city, extract corresponding moment data to generate pseudo document to determine city flood event;Linkage disaster city, build cross-city transmission chain;Based on flood propagation chain, flood event number caused by rainstorm, the number of affected cities, the start and end time of each city flood and cross-city chain are written into history flood disaster event database;The application can accurately identify and track flood events in space and time.

The application relates to the technical field of artificial intelligence and bioinformatics, in particular to a multi-source spatial multi-omics data integration method based on hierarchical graph contrast learning, which comprises the following steps: preprocessing acquired multi-source spatial multi-omics data, and constructing a spatial adjacency graph based on spatial distance; using a graph attention autoencoder to encode modality-specific features of the preprocessed multi-omics data based on the spatial adjacency graph, so as to obtain latent feature representation and negative representation of each spatial node; integrating the encoded latent feature representation and negative representation through hierarchical graph contrast learning; decoding and reconstructing the integrated features based on a neural network, and outputting reconstructed features for downstream tasks to minimize reconstruction error. The method can improve the full-scene data integration capability and improve the multi-omics data fusion precision.

The application belongs to the technical field of bioinformatics, and discloses a multi-modal cell deconvolution method and system based on priori driving. The method comprises the following steps: uniformly naming, deduplicating and intersecting filtering genes / proteins of reference single cell data and target data, and performing scale standardization in respective data domains; generating a pseudo bulk training set based on the scale-standardized reference single cell data; mapping sample expression to a pathway space based on a GMT file of an external knowledge base, the pseudo bulk training set and the target data; constructing a deconvolution model, and respectively performing forward processing on double-view data of a source domain or a target domain by using a double-branch encoder with shared weights, so as to complete cell deconvolution.

The application discloses a chemical reaction diagram analysis method and system, and relates to the technical fields of chemical informatics and multi-agent cooperation, which extracts structured entities of a chemical reaction diagram through a molecular agent, an arrow agent and a text agent, constructs a space-semantic diagram and a chemical perception diagram in parallel based on the entity set, aligns and fuses node and edge weights, generates a unified candidate edge set, and then performs conflict pruning in combination with space consistency, chemical consistency and arrow direction constraints to obtain a unified reaction reasoning diagram; candidate structure search based on a maximum weight generation forest algorithm is executed on the diagram, and the final reaction structure is determined through element conservation and formal charge balance auditing, and a structured reaction equation is output. The application can improve the analysis accuracy under a complex reaction topological layout.

The present application relates to the field of medical informatics, and provides a paliperidone efficacy prediction method and system based on plasma metabolomics. The method comprises the following steps: collecting basic data of a target patient; performing feature selection on metabolites through the basic data to obtain relevant metabolites corresponding to paliperidone efficacy; training a neural network based on the relevant metabolites and baseline data in the basic data to obtain a prediction model; and performing paliperidone efficacy prediction on a patient to be predicted through the prediction model to obtain a prediction result. The present application can predict the probability of patient response to paliperidone, thereby more accurately evaluating the efficacy of paliperidone before medication, and providing an auxiliary decision for individualized medication.

The invention discloses a gastrointestinal symptom identification method and a sensing system, and relates to the technical field of health informatics and medical information processing, and the method comprises the steps: collecting excitation response and abdominal sound to calculate an acoustic transfer function, and employing a main formant frequency to invert a quality loading index representing a condensation water film effect; constructing an equalization gain based on the transfer function to carry out spectral shape correction on the abdominal sound time-frequency spectrum; constructing a symptom evidence vector and performing adaptive weighted scaling on the symptom evidence vector by using a quality loading index; and finally calculating the distance between the weighted vector and the symptom mode prototype library and mapping the distance into a prompt probability. According to the method, the problems of acoustic link drift and signal distortion caused by microenvironment humidity change in long-time monitoring are solved, and the accuracy and robustness of symptom identification are improved.

This invention belongs to the field of healthcare informatics, specifically relating to a method and apparatus for the automatic generation and evaluation of psychological scales for different subject populations. The method is based on expert-designed expert factors, utilizing a large-scale model to perform semantic analysis and structural induction on existing psychological scales, extracting psychological constructs and construct dimensions to form a knowledge base of psychological scale items. Based on this knowledge base, and given the scale design requirements such as target psychological constructs and subject population characteristics, the method combines the item knowledge base with the structural validity constraint mechanism of the expert-designed psychological scales to achieve the generation of customized psychological scales for different subject populations. Furthermore, it proposes a multi-dimensional automatic evaluation system for psychological scales that meets psychometric requirements, covering multiple different dimensions, providing feedback information for scale content optimization, and realizing the transformation of psychological scales from manual design to automated and controllable generation.

An arrangement for automated testing of mobile devices comprising a learning arrangement for learning how to use test devices that do not match with an earlier already defined test case pattern. In the arrangement the learning arrangement generates instructions for performing a set of tasks. The tasks are then executed in the mobile device being tested. The mobile device provides feedback in form of error / success messages, screenshots, source code, return values and similar. Based on the feedback and earlier accumulated information the learning entity can generate a new set of instructions in order to execute the set of tasks successfully.

This invention relates to the field of medical device and physiological signal processing technology, specifically a low-frequency pulse data processing method for a graphene far-infrared eye care device. The method includes: constructing a multi-source physiological data acquisition layer to acquire substrate temperature data continuously acquired based on a first sampling frequency and discrete physiological impedance data from a single trigger sampling within an electrical stimulation feedback cycle during device operation; constructing a baseline prediction and feature separation layer to calculate the expected impedance baseline based on a physical mechanism data mapping model, and decoupling the physiological impedance data from the expected impedance baseline to extract dynamic residual data; and constructing a data decision layer based on health informatics to identify dynamic residual data according to a physiological state classification model, generate health assessment labels, and generate a digital control parameter matrix for adaptively updating the device's operating configuration. This invention can separate thermal drift from actual physiological changes, achieving stable identification of the user's eye physiological state under low power consumption conditions.

This invention discloses a method and system for single-cell transcriptome cell type annotation based on deep learning, belonging to the field of bioinformatics data processing technology. The method includes five steps: data quality control preprocessing, Transformer cell encoder pre-training, graph attention network cell relationship modeling, hierarchical classification annotation, and zero-shot transfer annotation. This invention deeply couples Transformer representation learning with graph attention networks, obtains general representations through masked gene prediction pre-training, enhances rare cell type features using KNN graphs and graph attention message passing, improves recognition accuracy by adopting a hierarchical classification architecture from main lineage to subtype, and supports zero-shot cross-modal annotation based on text description.

Infrastructure and methods to implement a platform for a horticultural operation are disclosed. Sensor data is received from one or more sensors configured to capture data for plants within a plant growth operation. Accumulated data associated with other plants in other plant growth operations is access. The data is analyzed to determine conditions of the plants within the plant growth operation. Plant grower actions to improve plant growth are determined. Instructions are transmitted to a controller device associated with the plant growth operation. Agricultural products or services associated with the plant grower actions are determined. An agricultural exchange service processes electronic commerce information from servicers of the products or services. Bids from the servicers are received, and selection and fulfillment of the bids are facilitated.

The present application relates to the technical field of data processing, in particular to a kind of spectrum radius feature extraction and Hamiltonity determination method for mixed graph structure.It includes the following steps: obtaining mixed graph data and constructing adjacency matrix, Laplacian matrix and unsigned Laplacian matrix;Three spectrum radii are extracted using power iteration acceleration algorithm, and a spectrum radius pyramid is constructed by multi-layer coarsening, forming a multi-scale spectrum feature vector;The feature vector is input into the classification model to output the Hamiltonity probability value;The confidence score is calculated by multiple random dropout, and it is dynamically decided whether to activate the accurate determination engine;Finally, the determination result is output.The present application combines multi-scale spectrum features and statistical features, uses graph neural networks to achieve high-precision probability prediction, and improves the reliability of boundary samples through confidence evaluation and cascade determination architecture, suitable for graph data analysis in the fields of communication networks and bioinformatics.

The present application relates to a kind of metabolic pathway extension network based on structural feature automatical establishment method, belong to bioinformatics and computational chemistry technical field.The purpose is to solve the problem of existing KEGG database incomplete, annotation lag of metabolite.Method includes: obtaining KEGG skeleton and modified group construction seed library;Through substructure matching and set difference calculation to determine the attribution of derivative;Iterative expansion forms structure association set;Combining stereochemistry rule and mass difference verification screening derivative;Merging KEGG reaction and extended relationship constructs network, and remove redundant path by graph theory method, finally output extended pathway network.The method greatly improves the annotation rate of metabolite pathway, supports in-depth functional analysis of metabolomics data.

The application discloses a large-scale mass spectrometry queue-oriented joint alignment and quantification method, relates to the cross field of bioinformatics, artificial intelligence and high-performance computing, and outputs a quantitative feature matrix through an end-to-end deep learning network; wherein, in a unified back propagation process, the end-to-end deep learning network synchronously optimizes the retention time alignment, the cross-batch intensity drift correction and the quantitative regression based on a joint loss function, the application regards the retention time alignment, the cross-batch intensity drift correction and the quantitative regression as a whole joint operation instead of linearly performing the operations one by one, and overcomes the defects of the step-by-step strategy in the prior art.

The present application relates to the technical field of medical informatics, and in particular to a portable AI psychiatric auxiliary recording device, a multi-modal sensing module collects audio signals of doctor-patient conversations, separates the voices of doctors and patients and extracts labeled acoustic feature vectors. The term enhancement module converts the acoustic features into text and outputs standard term annotations in combination with a psychiatric knowledge graph. The context reconstruction module analyzes long-range conversation dependencies to generate a structured semantic abstract. The template generation module fills in the medical record template based on the abstract and outputs a standardized draft. The meta-learning adaptation module monitors the output of each module and dynamically updates the parameters through distributed learning. The device uses beamforming, graph attention networks, Transformer-XL architecture and federated learning, etc. to solve the problem of unstructured voice analysis in psychiatric conversations, achieve accurate term mapping and automatic generation of medical records, improve accuracy and efficiency, and reduce the burden of manual review.

This invention proposes a drug target interaction prediction method based on graph interaction and multi-granularity fusion, belonging to the field of bioinformatics. Addressing issues such as oversmoothing of graph neural networks in heterogeneous graphs, insufficient utilization of drug-target interactions and multi-scale information, and poor adaptability of multi-branch feature splicing, an improved method is proposed. First, a deep interactive graph neural network branch is constructed on the node adaptive local smoothing features and the heterogeneous graph composed of drug-drug, target-target, and drug-target edges, realizing message passing and fusion of classification edges and introducing residual connections. Second, a dual-tower interactive branch is constructed for multi-order interactions. Third, a multi-granularity fusion branch is constructed to achieve progressive fusion of multi-scale features. Finally, the multi-branch representations are weighted and fused through a gating network, combined with the output results of a perceptron and a sigmoid function, and optimized using binary cross-entropy. This invention improves prediction performance and interpretability, and is applicable to scenarios such as drug discovery and target screening.

This application relates to the technical field of medical informatics, and in particular to a method and system for intraoperative consumable replenishment using surgical kits and a robot in collaboration. The method includes obtaining consumable reference parameters; extracting a basic kit template corresponding to the surgical type from a historical surgical record database based on the consumable reference parameters; weighting and scoring the consumable reference parameters according to preset weights to optimize the basic kit template and determine an initial pre-filled list; dynamically trimming the initial pre-filled list according to actual consumable production data to output intraoperative consumable data; collecting consumable usage data in real time and automatically, and comparing the usage data with the intraoperative consumable data to calculate the shortage quantity; if the shortage quantity exceeds 1, generating an automatic replenishment task, and triggering robot dispatch based on the automatic replenishment task. This application eliminates the need for manual application form completion; the system automatically completes a three-step closed loop of demand identification, shortage calculation, and task generation.