1460results about "Data visualisation" patented technology

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

A computer-implemented method of processing patient image data comprises: receiving a patient-specific vascular model of an individual, wherein the patient-specific vascular model is based at least in part on medical images acquired of the individual; receiving a patient-specific tissue model of a tissue of the individual; observing an actual perfusion value of the tissue; determining at least one vessel pathology that causes the actual perfusion value; and selecting a candidate updated vascular model of the plurality of candidate updated vascular models that results in a minimum difference between the hypothetical perfusion value and the actual perfusion value; updating the patient-specific vascular model to include the at least one vessel pathology of the selected candidate updated vascular model; and outputting an estimate of a location of the at least one vessel pathology or an estimate of a severity of the at least one vessel pathology.

Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of a location of disease in the patient's vasculature based on the received patient-specific data; identifying one or more changes in geometry of the anatomic model based on a modeled progression or regression of disease at the location; calculating one or more values of a blood flow characteristic within the patient's vasculature using a computational model based on the identified one or more changes in geometry of the anatomic model; and generating an electronic graphical display of a relationship between the one or more values of the calculated blood flow characteristic and the identified one or more changes in geometry of the anatomic model.

Systems and methods are disclosed for determining blood flow characteristics of a patient. One method includes: receiving, in an electronic storage medium, patient-specific image data of at least a portion of vasculature of the patient having geometric features at one or more points; generating a patient-specific reduced order model from the received image data, the patient-specific reduced order model comprising estimates of impedance values and a simplification of the geometric features at the one or more points of the vasculature of the patient; creating a feature vector comprising the estimates of impedance values and geometric features for each of the one or more points of the patient-specific reduced order model; and determining blood flow characteristics at the one or more points of the patient-specific reduced order model using a machine learning algorithm trained to predict blood flow characteristics based on the created feature vectors at the one or more points.

The invention discloses a drug target activation and inhibition relation prediction method based on a depth map neural network, and aims to improve the modeling precision and prediction performance of an activation or inhibition action mechanism between a drug and a target. According to the method, on the basis of a fine-grained graph interaction modeling mechanism, multi-scale structural characteristics of drug molecules and three-dimensional space structural information of protein residue levels are fused, and a heterogeneous interaction graph between drugs and proteins is constructed. The method comprises the following steps: firstly, acquiring a drug-target sample with an activation / inhibition tag through a public database, predicting a protein structure by utilizing AlphaFold2, and constructing a protein residue map and a drug molecular map; multi-scale structure semantic representation is obtained through sub-graph decomposition, atomic-scale feature extraction and graph neural network coding of drug graph features; protein graph node features are combined with context embedding generated by a pre-training language model, DSSP coding, secondary structure spectrum and atomic structure features are constructed, and edge features are designed based on the geometrical relationship between residues. Then, based on constraints such as spatial distance and biochemical similarity, a fine-grained mapping relation between drug atoms and protein residues is established, an interaction graph is constructed, and coding is carried out through a GraphSAGE network; and finally, fusing the interacted multi-source embedding, and completing the prediction of the activation / suppression relationship through a multi-layer perceptron. A cross entropy loss function, an Adam optimizer and hyper-parameter grid search are adopted in model training; in the evaluation stage, five-fold cross validation and an independent test set are adopted, and indexes such as the accuracy rate, the recall rate, the F1 score, the specificity and the Morse correlation coefficient are used for comprehensively evaluating the performance of the model. Experimental results show that compared with an existing method, the method has the advantages that the prediction accuracy and mechanism interpretability are remarkably improved, and the method has good generalization ability and application prospects and is suitable for multiple fields of drug action mechanism research, new drug discovery and the like.

Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of a location of disease in the patient's vasculature based on the received patient-specific data; identifying one or more changes in geometry of the anatomic model based on a modeled progression or regression of disease at the location; calculating one or more values of a blood flow characteristic within the patient's vasculature using a computational model based on the identified one or more changes in geometry of the anatomic model; and generating an electronic graphical display of a relationship between the one or more values of the calculated blood flow characteristic and the identified one or more changes in geometry of the anatomic model.

The invention is suitable for the technical field of tumor treatment and protection, and particularly relates to an intelligent classification prediction method, system and equipment for tumor treatment and protection, and the method comprises the steps: obtaining tumor-related molecular marker detection values and clinical diagnosis and treatment features of all target patients, and comprehensively integrating the multi-dimensional medical information of the patients; according to the relevance of molecular markers and clinical features, a scientific and quantitative multi-dimensional prognosis layering index set is constructed, and an accurate layering evaluation framework is formed; inputting the medical data into a pre-trained multilayer neural network model, and generating a high-resolution tumor characterization map through a cross-modal feature fusion technology; based on the dynamic matching of the atlas and the hierarchical index set, the hierarchical contribution degree parameter and the prognosis risk score are calculated in combination with the random forest model, the precise quantitative evaluation of the condition of the patient is completed, a high-credibility quantitative basis is provided for clinical decision making, the over-treatment risk is effectively reduced, and the prognosis management effect of the patient is improved.

The invention discloses a microbial fermentation strain breeding control system based on a neural network model, and relates to the technical field of digital fermentation, and the system comprises a thallus data acquisition module, a biological feature recognition module, a prediction classification module, a breeding decision module, an optimization training module, an early warning monitoring module and a sorting regulation module. The bacterial data acquisition module preprocesses bacterial data; the biological feature recognition module constructs a bacterial feature library; the prediction classification module predicts the adaptability of bacterial strains and classifies the bacterial strains; the selection decision module selects the bacterial strains and generates culture parameters; the sorting regulation and control module generates a three-dimensional scatter diagram and a hereditary character report. According to the method, the precision and efficiency of breeding of the microbial fermentation strain are remarkably improved, intelligence and automation of the breeding process are achieved, the labor cost is reduced, the stability and controllability of the fermentation process are enhanced, and powerful technical support is provided for innovative development of the microbial fermentation industry.

The invention relates to the field of data management and knowledge maps, and provides a civil administration data consanguinity knowledge map construction method and device based on a large model, and the method comprises the steps: constructing a basic entity network based on the metadata feature extraction of civil administration data; based on the basic entity network, analyzing the data table structure to generate a data table structure relation model; analyzing the complex structured query language statement, identifying field-level blood relationship, and generating a preliminary blood relationship graph; all physical source tables are traversed, virtual table redundancy and data node repetition are eliminated, and an optimized blood relationship graph is generated; and based on the optimized blood relationship graph, identifying public data nodes and combining field-level data blood relationships to generate a final civil administration data blood relationship graph. According to the method, the problems of difficulty in accurately analyzing complex SQL statements, processing virtual table redundancy and data node repetition in the prior art are solved, and high-precision and automatic civil administration data consanguinity knowledge graph construction is realized.

To provide favorable systems and methods for determining blood flow characteristics of a patient.SOLUTION: One method includes: receiving, in an electronic storage medium, patient-specific image data of at least a portion of vasculature of the patient having geometric features at one or more points; generating a patient-specific reduced order model from the received image data, the patient-specific reduced order model comprising estimates of impedance values and a simplification of the geometric features at the one or more points of the vasculature of the patient; creating a feature vector comprising the estimates of impedance values and the geometric features for each of the one or more points of the patient-specific reduced order model; and determining blood flow characteristics at the one or more points of the patient-specific reduced order model using a machine learning algorithm trained to predict blood flow characteristics based on the created feature vectors at the one or more points.SELECTED DRAWING: None

The invention discloses a drug target interaction prediction method based on a protein sequence. The method comprises the following steps: constructing a drug target database; constructing a target feature extractor based on a pre-trained protein language model; constructing a drug feature extractor based on a pre-trained molecular model; constructing a feature learning method by adopting a self-attention mechanism, constructing a drug target interaction learning method by adopting a cross attention mechanism, constructing a predictor in combination with a linear layer, and constructing a prediction drug target interaction model; training and optimizing parameters of the drug target interaction prediction model to obtain an optimized drug target prediction model; inputting a drug target into the optimized prediction model, outputting a prediction result, and finally marking potential active sites and important amino acid residues of the drug and the target through attention score visualization. According to the method, the precision of predicting the interaction between the drug and the target spot based on the protein sequence is improved, and the interpretability of the deep learning model is realized.

The invention belongs to the technical field of gene sequencing, and particularly relates to an efficient and high-throughput gene sequencing data processing system. The data processing system comprises a data acquisition module, a data preprocessing module, a data storage and management module, a data comparison and alignment module, a variation detection module, a data integration and analysis module, a visualization and report module and a quality control and verification module, and the data acquisition module is used for extracting DNA or RNA and comprises phenol chloroform extraction, column purification and magnetic bead extraction; and detecting the concentration and the quality of the sample by using a spectrophotometer or a fluorescence quantifier, and carrying out pollution removal and degradation treatment on the extracted nucleic acid. According to the method, a comprehensive, accurate and efficient data processing solution can be provided for genomics research by optimizing the data processing flow, improving the data quality, enhancing the analysis capability and supporting automation.

The invention discloses a biological hazard big data analysis and monitoring early warning system, and relates to the technical field of public health safety, and an analysis subsystem in the system comprises a core logic module comprising a quality control unit, an error correction unit, an assembly unit and a box separation unit; the unit analysis module comprises a pathogen analysis unit, a resistance gene unit, a virulence evaluation unit and an evolution development unit; the flora integrated analysis module comprises a traceability analysis unit, a mutation characteristic unit, a propagation evolution unit and a transformation management and control unit; the early warning subsystem comprises a risk assessment and early warning system design unit, a dynamic research and propagation analysis unit, an assessment model construction unit, a pathogen evolution and function research unit, a toxicity and propagation risk comprehensive prediction unit, a pathogen risk monitoring network unit and an unknown pathogen and potential risk identification unit. According to the method, the biological hazard data can be comprehensively, efficiently and accurately analyzed.

Systems and methods described herein include embodiments for generating a geometrical structure of a binding complex formed between a plurality of macromolecules, comprising: processing an input representation comprising a plurality of representations of the plurality of macromolecules to generate a geometry prior; sampling an initial geometrical structure of the binding complex based on the geometry prior; and processing, using a neural network, the initial geometrical structure to generate the geometrical structure of the binding complex formed by the plurality of macromolecules.

The invention discloses an aquatic organism diversity rapid evaluation system and method based on GIS and eDNA technologies, and the method comprises the steps: selecting a plurality of sampling points in a target region to collect water samples, and integrating and processing the geographic information data of the sampling points and surrounding regions by adopting a GIS system; extracting eDNA from the collected water sample, performing amplification and sequencing on the eDNA by adopting a high-throughput sequencing technology, obtaining species and relative abundance of the species existing at the sampling point, and forming a species list; importing species distribution information corresponding to the species list into a GIS system, and obtaining a species distribution area and species types; according to the method, a species distribution prediction model is established, aquatic organism diversity change trends of the hot spot area and the potential ecological threat area are monitored in real time, an evaluation report and protection suggestions are formed, and protection measures are updated in time, so that the data acquisition and processing efficiency is improved, and the accuracy of species identification and the comprehensiveness of diversity evaluation are enhanced; and a real-time monitoring and early warning mechanism is provided.

The invention discloses a rapid sequencing and traceability analysis system for input infectious diseases, which relates to the field of customs quarantine and comprises a sample preprocessing unit, a high-throughput sequencing unit, a data processing and quality control unit, a rapid comparison and annotation unit and a traceability analysis unit. According to the input infectious disease rapid sequencing and traceability analysis system, a complete link is formed from sample preprocessing, library construction, data cleaning, pathogen recognition and transmission map construction, module splitting and manual intervention are avoided, and tasks can be automatically completed in large-scale and emergency scenes.

The present disclosure relates to systems, non-transitory computer-readable media, and methods that train and utilize machine learning models to generate perturbation embeddings from phenomic images of cells, including neuronal cell images. Indeed, in one or more implementations, the disclosed systems generate a perturbation embedding using an adapter model or a mixture of experts model. In some implementations, the disclosed systems utilize a mixture of experts model that combines phenomic embeddings from different embedding models to generate a mixture of experts phenomap that contains information from multiple embedding models.

The invention discloses a colorectal cancer drug relocation method based on multi-omics integration. The system comprises a multi-omics data acquisition and preprocessing module, a tumor microenvironment analysis module, a specific disease network construction module, a multi-dimensional drug relocation module and a result evaluation module. And the tumor microenvironment analysis module comprises cell heterogeneity identification, cell map construction, cell annotation and tumor cell subset annotation. The specific disease network construction module comprises tumor feature expression program extraction, expression program screening, meta-program construction, clinical related meta-program recognition and specific disease protein interaction network construction. And the multi-dimensional drug relocation module comprises a module for identifying diseases by using a random walk algorithm, carrying out drug screening based on disturbance data, carrying out drug screening based on network proximity and carrying out comprehensive drug relocation. From the perspective of single cell data, element programs related to colorectal cancer survival are excavated, corresponding modules are designed, and the efficiency and precision of colorectal cancer targeted drug screening are improved.

The invention relates to the technical field of bioinformatics, and provides a visual analysis method and system for a rice multi-tissue single cell expression profile. The method comprises the following steps: comparing sequencing data of an original single cell transcriptome of a rice tissue to obtain a standardized transcriptome data set; performing batch effect correction and integration on the standardized transcriptome data set to obtain a whole plant expression matrix; performing cell type annotation on the whole plant expression matrix to obtain a cell type annotation system; carrying out visual dimension reduction processing on the whole plant expression matrix fused with the cell type annotation system, and carrying out co-expression network construction to obtain a modular tissue correlation analysis model; and establishing an interaction end based on the module organization correlation analysis model, and realizing data visualization analysis through the interaction end. The invention provides a one-stop analysis platform for rice cell heterogeneity research, functional gene mining and molecular breeding.

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 relates to a method for predicting river nitrogen metabolism gene abundance and key influence factors thereof based on automatic machine learning. Comprising the following steps: collecting a river water sample, detecting water quality physicochemical indexes, and performing metagenome sequencing to obtain nitrogen metabolism gene abundance; taking conventional water quality physicochemical indexes as explanatory variables, taking nitrogen metabolism gene abundance as a target variable, adopting automatic machine learning to construct a model, evaluating the performance of the model through a mean square error (MSE) and a fitting coefficient (R), and selecting an optimal algorithm for prediction; the model analyzes the relative contribution of each water quality index through an SHAP value graph, and reveals the relationship between each water quality index and gene abundance by using a partial dependency graph (PDP). The method shows high precision and good generalization performance in the aspect of predicting the abundance of the nitrogen metabolism gene, and is suitable for describing the nitrogen dynamic process of a river water body. The model can explain, analyze and quantify the influence of different water quality indexes on the nitrogen metabolism gene afterwards, and a scientific basis is provided for river water quality management and nitrogen metabolism optimization regulation and control.

The invention relates to the technical field of single cell data processing, in particular to a database visualization method based on single cells. The method comprises the following steps: acquiring a single cell data set; standardizing the single cell data set to obtain standardized single cell data; performing high-dimensional feature extraction on the standardized single-cell data to obtain single-cell high-dimensional feature data; and performing nonlinear dimension reduction processing on the single-cell high-dimensional feature data to obtain single-cell dimension reduction data. Performing data clustering on the single cell dimension reduction data to obtain single cell clustering data; performing gene differential expression extraction on the single cell clustering data to obtain differential expression gene data; multi-modal data fusion analysis is carried out on single cell data through a data processing technology, a feature mapping technology and a visualization technology; and dynamic weight division is carried out on the single cell database, so that the effect of visualization of the single cell database is enhanced.

The invention discloses a large-model-driven intelligent biosynthesis system and method, and the system employs a centralized multi-large-model intelligent agent architecture, and executes a task through a modularized intelligent agent. The system is composed of a man-machine cooperation interaction module, a global task management module, a general information retrieval module, a reverse path planning module, a protease modification module, a metabolic pathway optimization module, a wet experiment tool execution module and a code execution module. Each module integrates different tools and data sources for specific task fields. By means of the system, complex biosynthesis experiment design, planning and execution can be completed autonomously, functional modules and wet experiment feedback can be dynamically adapted, and the flexibility of experiment planning is ensured.

The invention relates to the technical field of biological breeding, in particular to a biological breeding management method and system based on big data. The method comprises the following steps: acquiring original germplasm data; multi-source heterogeneous data integration is carried out on the original germplasm data, the data integration comprises genotype sequencing integration and historical phenotype integration, and integrated germplasm data is generated and stored in a database in a distributed mode; obtaining original growth data, performing data preprocessing, generating standardized germplasm resource comprehensive data including a crop image feature set and an environment response parameter set, and synchronizing the data to the database; and performing whole genome selection prediction on the integrated germplasm data based on the standardized germplasm resource comprehensive data to generate a genotype-phenotype prediction model. By integrating germplasm resources, field management, breeding management, phenotype management, genotype management and whole genome selection, the accuracy and adjustment flexibility of biological breeding management are improved.

To provide favorable systems and methods for determining blood flow characteristics of a patient.SOLUTION: One method includes: receiving, in an electronic storage medium, patient-specific image data of at least a portion of vasculature of the patient having geometric features at one or more points; generating a patient-specific reduced order model from the received image data, the patient-specific reduced order model comprising estimates of impedance values and a simplification of the geometric features at the one or more points of the vasculature of the patient; creating a feature vector comprising the estimates of impedance values and the geometric features for each of the one or more points of the patient-specific reduced order model; and determining blood flow characteristics at the one or more points of the patient-specific reduced order model using a machine learning algorithm trained to predict blood flow characteristics based on the created feature vectors at the one or more points.SELECTED DRAWING: None

The invention discloses a drug target affinity prediction system based on cross-modal feature fusion, and relates to the technical field of biological information. The invention aims to solve the problem of low prediction precision of the existing DTA prediction method. The method comprises the following steps: preprocessing a drug SMILES character string and a protein amino acid sequence to obtain a drug molecular map, a drug SMILES sequence embedding characteristic, a protein contact map and a protein amino acid sequence embedding characteristic; according to the drug molecular diagram and the protein contact diagram, drug diagram modal characteristics and protein diagram modal characteristics are obtained; obtaining drug sequence modal characteristics and protein sequence modal characteristics by using drug SMILES sequence embedding characteristics and protein amino acid sequence embedding characteristics; fusing the drug pattern modal features and the drug sequence modal features to obtain drug fusion features, and fusing the protein pattern modal features and the protein sequence modal features to obtain protein fusion features; and acquiring the drug target affinity by using the drug fusion feature and the protein fusion feature. The method is used for predicting the affinity of the drug target.

The invention discloses an lncRNA-miRNA association prediction system and method fused with a hypergraph perspective, and belongs to the technical field of biological information. The LncRNA-miRNA association prediction method aims at solving the problem that in an existing LncRNA-miRNA association prediction method, the node representation capacity is insufficient, interaction relation modeling is not comprehensive, and consequently prediction performance is poor. According to the method, lncRNA and miRNA sequence data are obtained and subjected to data preprocessing, then K-mer frequency features, Doc2Vec semantic features, CTD structural features and Role2Vec topological features of each RNA are obtained, multi-level feature fusion is carried out to obtain unified fusion features of each RNA, then the unified fusion features are sent into GCN and HGCN networks to obtain respective corresponding features, optimization is carried out on the basis of comparative learning optimization, and the fusion features of each RNA are obtained. And then dynamic fusion is carried out, final fusion feature representation is obtained based on the gating factor, and lncRNA-miRNA association prediction is realized based on the final fusion feature representation.

The invention relates to the technical field of medical information, in particular to a construction method and system of a liver cancer gene database and a medium, and the method comprises the following steps: constructing a spatial neighborhood relationship based on cell space coordinates, and defining mutually communicated cell populations as spatial neighborhood hyperedges; labeling functional modules according to the gene expression values, and associating cell nodes in the same functional module as functional module hyperedges; constructing a space-time dynamic hypergraph structure based on the spatial neighborhood hyperedge and the functional module hyperedge; and carrying out local aggregation on node features of the space-time dynamic hypergraph structure to obtain a gene expression correlation weight matrix between a target node and a domain node so as to construct a liver cancer gene database. According to the method, a high-quality data support and analysis platform is provided for liver cancer mechanism research by comprehensively describing a spatial interaction network of liver cancer cells, dynamic evolution of functional modules and gene expression correlation.

Embodiments described herein relate to systems and methods for mapping molecules into interfaces. An example interface includes a visual interface having a map visualization. Example molecules include proteins, or any protein-like molecules or fragments thereof such as antibodies, antigens, proteins, lectins, receptors. Embodiments described herein relate to systems and methods for mapping molecules into interfaces by processing data using dimensionality reduction to generation representations of molecule maps (e.g. map visualizations).

The invention relates to the technical field of bioinformatics, in particular to a biological information analysis system based on a large model technology, which comprises a data analysis calibration module, a problem disassembly module, an analysis task arrangement module, a result mapping module and a feedback iteration module. According to the method, genome comparison and clinical phenotype timestamps are dynamically calibrated, time sequence dislocation deviation is eliminated, base complementary pairing is combined with protein network anomaly screening, low-abundance collaborative variation capture is enhanced, genotype-phenotype discrete distribution quantifies and unifies multi-modal data benchmark, and the problem of multi-source heterogeneous standardization deficiency is solved; the method comprises the following steps: classifying and integrating pathogenic gene semantic weights by structural variation, balancing a statistical threshold and a biological function, dynamically optimizing an analysis sequence, synchronously covering a key mutation region, improving function annotation of a non-coding region, integrating gene expression clustering and protein network topology in a three-dimensional distribution manner, breaking through two-dimensional space limitation, performing closed-loop feedback to correct a threshold iteration elimination rule, and finally obtaining a high-quality gene expression cluster. And the genetic heterogeneity false positive rate is reduced.