1095results 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.

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 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 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.

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 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.

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 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.

The invention relates to the technical field of collaborative prediction, in particular to a plant polygene stress resistance collaborative prediction method and system based on a federal map neural network. The method comprises the following steps: modeling a multilayer heterogeneous graph according to acquired plant genome data; constructing a dynamic graph neural network model based on time sequence perception; training a dynamic graph neural network model by using the multi-layer heterogeneous graph, and performing distributed privacy calculation on the multi-layer heterogeneous graph by using a federated graph neural network; generalization training is carried out on the small sample scene data based on multi-task learning and a cross-species migration mechanism; and obtaining a gene function prediction result. Through a multi-layer heterogeneous graph fusion technology and a dynamic graph neural network design of time sequence perception, multi-dimensional biological information such as gene regulation, protein interaction, metabolic pathways and stress response can be captured at the same time, and time sequence change characteristics of gene expression in the plant stress response process can be captured at the same time.

The invention provides a cell specific transcription factor regulatory network analysis method and a visualization platform, and relates to the technical field of bioinformatics, the method comprises the following steps: constructing a gene regulatory network through a GRNBoost2-cisTarget-AUCell-Cell GRN workflow based on a transcription factor in combination with a motif database; screening a direct regulation relationship in combination with the database, and calculating an activity score of a regulator in each cell; based on activity scores and cell type annotation results, grouping the single cell data by using a unified manifold approximation and projection (UMAP) dimensionality reduction method and a Leiden clustering algorithm, and displaying the following results through an interactive visualization tool: a cell clustering UMAP graph, performing color marking according to cell types; a UMAP graph and a heat map of transcription factor regulator activity; according to the visual map of the gene regulation and control network, transcription factors and target genes are distinguished through node shapes, and regulation and control relations are marked through line weights and colors. According to the invention, an accurate regulation and control network can be provided.

The embodiment of the invention provides an end-to-end B cell clone pedigree forest construction method and related equipment, and can be applied to the technical field of data processing. According to the method, a plurality of obtained receptor sequencing sequences are subjected to germline comparison identification to obtain a first test Fv sequence corresponding to each receptor sequencing sequence, and a germline Fv sequence corresponding to each receptor sequencing sequence is generated; performing integrity filtering on the first test Fv sequence, performing clone type division to obtain a plurality of first clone type sets, constructing corresponding first evolutionary trees to form a first pedigree forest on the basis of a second clone type set contract type conversion probability, and performing node optimization on all the first evolutionary trees to obtain a second pedigree forest; and after it is determined that the homotype category conversion probability after updating based on all the second evolutionary trees meets the preset requirement, visualization processing is performed on all the second evolutionary trees, so that the systematic cognition efficiency of related personnel on the adaptive immune response mechanism can be improved.

The invention discloses a molecular modification design method for improving the catalytic activity and the thermal stability of citrobacter vinegaticus-derived hyaluronidase and expression application of the citrobacter vinegaticus-derived hyaluronidase. The interaction of citrobacter vinegaticus hyaluronidase active pocket amino acid residues and hyaluronate tetrasaccharide molecules is visually explored through Pymol, conservative analysis of pocket amino acid evolution is carried out through Consurf, and rational design is carried out through policies of FoldX for calculating enzyme-substrate binding free energy, so that the hyaluronidase catalytic activity is improved. A mutant with significantly improved enzyme activity is obtained through screening, and the thermal stability of the enzyme is further improved through PROSS design and an analysis strategy of mutation site amino acid evolution conservative property and position. According to the invention, the sequence, structure and function of hyaluronidase are deeply studied, and hyaluronidase is mutated from two aspects of stabilizing a catalytic structure and promoting a catalytic reaction, so that a high-activity enzyme with higher application value in industrial production is obtained.

The invention discloses a positioning method of trachinotus ovatus growth trait related QTL (quantitative trait loci) based on 2b-RAD technology and application, the method uses 2b-RAD technology to perform sequencing on 300 trachinotus ovatus full-sib F1 generation and male and female parent individuals and develop SNP (single nucleotide polymorphism) markers, constructs a trachinotus ovatus high-density genetic linkage map, performs linkage positioning analysis in combination with 8 growth phenotype data, and finds that the trachinotus ovatus growth trait related QTL is found. 85 stable QTLs associated with the growth traits are screened out, and the stable QTLs contain 763 SNP sites. The invention also discloses an application of the method in a genetic map of trachinotus ovatus growth character positioning or molecular marker-assisted breeding of trachinotus ovatus, and the SNP molecular markers of trachinotus ovatus can be applied in genetic map construction and growth character positioning. Particularly, the method has a good application prospect in trachinotus ovatus growth trait molecular marker assisted breeding.

The invention discloses a circular RNA drug sensitivity correlation identification method based on integrated multi-instance learning. The circular RNA drug sensitivity correlation identification method comprises the following steps: collecting experimental verification circular RNA and drug sensitivity correlation data; establishing a feature representation model based on a heterogeneous network and vertexes; embedding a heterogeneous graph node into the model, and extracting deep feature representation of the node; designing a meta-path instance embedding projector, and generating a plurality of meta-path instances; a circular RNA and drug sensitivity association predictor is constructed and completed; constructing an integrated heterogeneous graph network deep learning model; and outputting the meta-path instance of the circular RNA and drug pair and the attention coefficient, and carrying out interpretable analysis. According to the method, integrated learning and a deep learning model are combined, so that the reliability of the model is improved; according to the invention, interpretable analysis is carried out by utilizing the meta-path and the attention coefficient, the potential action mechanism of the circular RNA associated with the drug sensitivity can be explained, and the guidance of medical research is facilitated.

The invention discloses a method for depicting and analyzing a heavy non-aqueous phase pollution site based on microbial structure information, which comprises the following steps of: based on a historical geological survey report and a historical leakage event, defining a pollution analysis area, and constructing a site pollution conceptual model by combining high-density resistivity with a stable isotope tracer method; performing sample collection and detection on the target area to obtain area sample detection information; performing microflora analysis according to the regional sample detection information, and judging a potential pollution retention region of the target region to obtain pollution region analysis information; carrying out multi-source data coupling by combining pollution area analysis information and area sample detection information, and carrying out pollution field three-dimensional description on a target area to obtain an area DNAPLs pollution condition diagram; a degradation function gene interaction network is constructed, restoration potential grading is performed on a target area, and area restoration suggestion is performed, so that the limitation of a traditional investigation method is broken through, and accurate analysis and restoration assistance of pollution distribution are realized.

The invention provides a salty peptide classification screening method and system based on peptiomics and machine learning. The method comprises the following specific steps: (1) obtaining a peptide sequence to be analyzed; (2) calculating basic physicochemical properties and sequence characteristics of the peptide sequence, and extracting high-dimensional embedding characteristics of the peptide sequence based on an evolution scale modeling model; (3) carrying out feature fusion on the basic physicochemical properties, the sequence features and the high-dimensional embedded features obtained in the step (2) to obtain a fused feature vector; (4) performing feature screening and importance sorting on the fused feature vector by using a random forest and an XGBoost and Extra Tres ensemble learning model to obtain a key feature subset; and (5) inputting the key feature subset into the constructed salty peptide classification prediction model, and outputting a salty peptide prediction result. According to the method, a multi-dimensional feature extraction algorithm is combined with a deep learning model, a salty peptide prediction model is constructed, and accurate identification and functional characteristic analysis of salty peptides are realized.

The invention discloses an administration agent in-vivo release and absorption visualization method based on a CFD-PBM coupling model. The method comprises the following steps: constructing a CFD model of a physiological environment of an injection site; establishing a population balance model (PBM) of the drug particles; the PBM is embedded into a CFD model solver, multi-scale coupling simulation is carried out, and a CFD-PBM coupling model is obtained; the three-dimensional visualization engine dynamically displays drug concentration distribution and particle behaviors; reversely adjusting preparation prescription parameters by using a multi-parameter optimization algorithm; and calibrating model parameters, and generating a key data report. According to the method, the prediction precision and the research and development efficiency can be remarkably improved, and the method is suitable for development of long-acting preparations such as microspheres and implants.

The invention discloses a crop adaptability evaluation model based on a genotype-environment interaction effect and application of the crop adaptability evaluation model, and relates to a biological information and artificial intelligence cross technology. Carrying out data preprocessing, environment mean value calculation and centralization, constructing a linear regression model, obtaining parameters representing adaptability, carrying out model verification and output, and carrying out adaptability grading; the average performance of the material is measured through the intercept, the slope reflects the response degree of the material to the environmental change, and the two jointly determine the environmental adaptability characteristics of the material. The invention provides a robust method for processing noise and missing values in a multi-environment test, establishes a unified adaptability quantitative standard to classify the adaptability of different materials, is beneficial to improving the directionality and accuracy of crop breeding, and accelerates the breeding process of high-yield and stable-yield corn varieties.

The invention provides a protein language model pre-training and protein mutation method and related products. According to one specific embodiment of the protein language model pre-training method, a sample protein data set is obtained; generating a multi-sequence alignment probability distribution sequence corresponding to the sample protein data according to the amino acid residue probability distribution of multiple sequences in each sample protein data alignment at each site; the sample protein sequence in each sample protein data and the corresponding multi-sequence comparison probability distribution sequence and structure sequence are sequentially connected in series in the forward direction or the reverse direction, and a multi-modal sequence corresponding to the corresponding sample protein data is generated; and finally, performing autoregression pre-training on the protein language model based on the multi-modal sequence corresponding to each sample protein data to obtain a pre-trained protein language model. Namely, the prediction performance of the model is improved by introducing a multi-sequence comparison probability distribution sequence as an independent intermediate reasoning mode and thinking chains in two directions.

An ensemble of predictive models that elucidate how cancer mutations impact the response to common replication stress-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multi-drug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK (receptor tyrosine kinase)-JAK-STAT assembly governing resistance. This invention defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine.