441results about "Data visualisation" patented technology

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 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 bacterial selenoprotein online resource platform, an application method, a terminal and a medium, and relates to the technical field of biological medicine, the online resource platform is deployed on a server, the cloud server is a Ubuntu cloud server, and Nginx, Waitpress and Flask are configured; the server side is in butt joint with a background resource, and the background resource is in butt joint with the constructed bacterial selenoprotein database; the server is in butt joint with a user interface of the front end, the user interface designs a corresponding interface framework and an interaction function based on static resources hosted by Nginx, and the interaction function is used for realizing query, analysis and use of relevant information of the bacterial selenoprotein. According to the method, a convenient online access channel of an integrated database and a real-time data analysis tool are provided for users in related fields, and important support is provided for accurate annotation of selenoprotein genes in a bacterial genome plan.

The invention discloses the technical field of pathology and space transcriptomics, and particularly relates to a gene expression prediction method and system based on multi-modal comparative learning and a guidance mechanism. Cutting the histological slice image into image blocks according to space coordinates; according to the method, a local convolution branch and a global Transform branch are combined to extract image features, the image features are mapped to a shared potential space through projection, soft contrast, hard contrast and global consistency constraints are introduced into the space, and cross-modal alignment of an image modal and a gene expression modal is realized; an expression prediction head is introduced in the training stage, representation learning is directly guided by a regression signal, and the relation between feature learning and gene expression prediction is broken through; in the inference stage, k-nearest neighbor retrieval and a multi-distance weighted aggregation strategy are combined to infer a gene expression profile of an unknown position. According to the method, the accuracy and robustness of space gene expression prediction can be effectively improved, the tissue space heterogeneity structure is kept, and the method has high clinical application and scientific research and popularization value.

The invention relates to a network prediction model for identifying a new cancer gene, a model construction method and application, and belongs to the field of biostatistics. According to the method, genomics, transcriptomics and proteomics data are integrated, a dynamic tensor twinning graph neural network is constructed, a hybrid multilayer random block model is utilized to perform tensor decomposition to extract global and local community features, causal and non-causal information is separated, causal feature mutual information is maximized, and non-causal interference is inhibited, so that the dynamic tensor twinning graph neural network is obtained. The model interpretation force is enhanced; through a dynamic community perception algorithm and a twin graph neural network, inter-layer graph dissimilarity is learned by using a graph similarity function, accurate detection of network structure mutation nodes in a high-frequency dynamic scene is realized, and an independent state transition and co-evolution mode is synchronously positioned. And the biological functions of the candidate genes are verified by combining KEGG pathway enrichment analysis and an independent database. Compared with an existing optimal algorithm, the mutation detection accuracy and the calculation efficiency are remarkably improved, and the AUROC and the AUPRC are both higher than those of an existing advanced recognition method.

The invention discloses a YOLO-Drop-based 3D printing droplet microfluidic immunoassay method, which organically integrates a high-precision microfluidic chip preparation technology, a droplet-level immunoreaction system and a fluorescence imaging acquisition and deep learning detection algorithm. And full-chain closed-loop processing from droplet generation, fluorescence signal acquisition, image recognition, result statistics to concentration quantitative analysis is realized. Through deep combination of chip physical parameters and model input features, the method not only significantly improves the accuracy and stability of droplet identification and concentration quantification, but also enhances the robustness of the algorithm under complex sample conditions through multi-scale feature fusion and a signal dual screening mechanism. Meanwhile, the optimized YOLO-Drop model supports embedded efficient reasoning, so that the whole analysis process has the characteristics of real-time performance and high throughput, and the method is suitable for rapid quantitative detection of various biomarkers.

Disclosed is a computer-implemented method for identifying small molecule binders within a DNA-encoded library (DEL) dataset. Said method comprises: obtaining a DEL dataset comprising enriched compounds; grouping enriched compounds into one or more compound groups based on a grouping criterion selected from at least one of: chemotype, library, enrichment level, or linear feature; generating a plurality of three-dimensional (3D) conformers, for each enriched compound in each compound group; comparing generated 3D conformers within one or across two or more compound groups to identify consensus overlays based on similarity metrics exceeding a predefined threshold; and selecting small molecule compounds forming said cross-group consensus overlays as small molecule binders having affinity for a target of interest. Disclosed also is a system for identifying small molecule binders within a DEL dataset. Said system comprising: a processor configured to perform aforementioned steps of method and an interface for displaying selected small molecule binders.

The invention belongs to the technical field of ecological influence evaluation of water conservancy and hydropower engineering, and particularly relates to an aquatic food web interference simulation method for water conservancy and hydropower engineering characteristics. Comprising the steps of species background information acquisition, ingestion function class group classification, aquatic food web construction, aquatic food web interference attack, interference attack influence network propagation, aquatic food web network structure and function monitoring and simulation result output. Compared with an existing method, a food web disturbance attack mechanism and a chain propagation mechanism are introduced in the simulation method, the cumulative ecological response can be described, the interference characteristics of water conservancy and hydropower engineering are effectively simulated, and the engineering adaptability of disturbance simulation is enhanced.

The invention discloses a bioreactor multi-parameter adaptive control system and method based on machine learning, and belongs to the technical field of bioreactor intelligent control, and the bioreactor multi-parameter adaptive control system comprises a bioreactor hepatocyte culture unit, a data acquisition and control unit and a machine learning intelligent decision-making unit. And the data acquisition and control lower computer controls operation variables such as gas supply, sampling and perfusion in the culture unit execution mechanism, collects culture process data returned by the parameter detection module, constructs a data-driven cell state prediction model by utilizing machine learning, and optimizes model parameters and operation variables based on a prediction result to realize closed-loop control. The method can overcome the defect that there is no precise mechanism model for hepatic cell culture, and multi-parameter collaborative optimization can be realized only depending on process data, so that the state of the hepatic cells is sensed in real time, full-cycle optimization of cell viability and functions is realized, and the activity and density of hepatic cell culture are effectively improved.

The invention provides a multi-view space transcriptomics clustering method based on sharing-specific information mining. The technical problems that in an existing space domain recognition method, robustness of a single view is insufficient, multi-view information fusion is insufficient, and description of data statistical characteristics is not accurate are solved. According to the technical scheme, the method comprises the following steps: S1, acquiring and preprocessing original data of a spatial transcriptome; s2, constructing a sharing-specific decomposition coding network; s3, using a ZINB expression decoder, a structure decoder and a Student's t distribution clustering module to construct a multi-task loss function; and S4, outputting a spatial domain division result based on the trained network. According to the method, the spatial domain recognition precision and robustness are remarkably superior to those of an existing method, the data statistical characteristics and biological significance can be accurately described, stable support is provided for tissue function analysis and tumor microenvironment research, and the analysis quality and application value of spatial transcriptomics data are effectively improved.

The invention discloses a multi-layer heterogeneous network unicellular organism network inference method based on meta-path enhancement, which mainly comprises a gene regulation knowledge base enhanced multi-layer heterogeneous network construction module for integrating an external gene interaction network and multiple omics data such as scRNA-seq, scATAC-seq, ST and the like; constructing a single-cell multi-omics multilayer heterogeneous network containing cell-cell, cell-gene and gene-gene relationships, and fusing spatial constraints to consider cell positions and tissue structures; and the feature enhancement module based on the meta-path explores complex semantics of the network by designing a multi-hop meta-path mode, designs an adaptive multi-view learning framework and a multi-round enhancement mechanism, and optimizes feature representation by using cell-gene interaction and cross-modal attention fusion. The unicellular biological network can be effectively deduced, the deduction accuracy and biological interpretation are remarkably improved, the method plays an important role in understanding the cell biological process, developing and treating diseases and the like, has good expandability, and can further integrate multi-modal omics data such as proteomics and metabonomics.

The invention discloses a hepatocellular carcinoma prognosis and immune response prediction method based on multi-omics machine learning, and relates to the technical field of biomedicine and artificial intelligence crossing, and the method comprises the following steps: S1, constructing a multi-omics data set for model training and verification; s2, performing feature integration and clustering analysis on the multi-omics data to obtain corresponding hepatocellular carcinoma molecular subtype distribution; s3, constructing a hepatocellular carcinoma prognosis model based on Cox regression combined with a random survival forest, identifying 11 core immune genes and corresponding weight coefficients by training the model, and constructing an immunotherapy response index IMLIRI score; and S4, carrying out clinical application on the IMLIRI score. According to the method, through multi-omics data integration and multi-queue external verification, the influence of data deviation and queue heterogeneity on the model performance is reduced, so that the method shows stable prediction performance in hepatocellular carcinoma queues with different sources and different pathogenesis backgrounds, and the reliability and generalizability of the model in clinical application are improved.

The application provides a method for analyzing the synergistic mechanism of hepatotoxicity and nephrotoxicity of non-steroidal anti-inflammatory drugs. The method comprises the following steps: preliminary toxicity prediction of NSAIDs and collection of toxicity target points, collection of liver and kidney disease target points, then cross and screening of the target points to obtain core target points and common core target points of NSAIDs induced liver and kidney diseases, and then constructing a protein interaction network of the common core target points; enrichment analysis of the common core target points to obtain the common action pathway of NSAIDs induced liver and kidney diseases; finally, further screening of the common core target points to obtain the key target points of NSAIDs induced liver and kidney diseases, and verification by using molecular docking technology. Compared with the traditional method, the advantages of the method are: first, the method does not depend on large-scale patient clinical data and a large number of animal or cell experiments, avoiding the ethical controversy in animal experiments and human experiments; second, the method can identify the potential cross-pathway and synergistic toxicity mechanism when a compound triggers multiple diseases, which is helpful for more comprehensive evaluation of the toxicity risk of NSAIDs.

Disclosed herein are methods for improving detection and monitoring of human diseases. The methods can be used to provide spatial and / or developmental localization of the source of each differential mutation within the body. The methods can also be used to generate a mutation map of a subject. And the mutation map can be used to monitoring state(s) of health of one or more tissues of a subject.

The invention discloses a gene line design system based on a four-library separation data structure, and the system comprises an element library which is used for storing the standardized description information of reusable biological elements; the regulation and control relationship type library is used for defining regulation and control relationship types among the biological elements; the regulation and control case library is used for storing regulation and control logic cases; and the line topology library is used for managing the topological relation between the gene line and the components thereof and the regulation and control logic. Wherein the element library, the regulation and control relationship type library, the regulation and control case library and the line topology library are associated through preset unique identifiers. According to the method, the gene line data are split into four libraries which are completely logically and physically separated, so that file lightweighting, highly readable results, easy maintenance and convenient reuse are realized, cross-file accurate reference is supported, and data consistency is improved.

To provide a method for compression of genome sequence data, a hardware processor, a system, and a storage device.SOLUTION: The method includes the steps of: determining whether leads of sequences of nucleotides or bases that have been aligned to a reference sequence are perfectly or imperfectly mapped with the reference sequence or whether the leads are unmapped with the reference sequence; and encoding the perfectly mapped leads according to a first encoding process and encoding the unmapped leads according to a second encoding process. In the determining step, for each imperfectly mapped lead, the number of mismatches between the lead and the reference sequence is compared with a threshold value. In the encoding step, the imperfectly mapped leads, when the number of mismatches is larger than the threshold value, are encoded according to the second encoding process, and when the number of mismatches is smaller than the threshold value, are encoded according to a third encoding process.SELECTED DRAWING: Figure 1

This invention discloses a peanut quality trait selection breeding method and its application based on whole-genome SNPs, belonging to the field of crop breeding technology. The specific steps of the method are: data acquisition, SNP optimization and marker screening, model construction, and new line breeding. This invention integrates whole-genome resequencing, SNP optimization and marker screening, and hybrid deep learning model construction to achieve prediction of peanut quality traits and evaluation of breeding values. Through whole-genome resequencing and quality control processes, an SNP variation map is obtained, covering ten key quality traits such as protein, oil content, and oleic acid, providing comprehensive genetic information for subsequent analysis. The SNP optimization and marker screening process selects loci to form a feature marker set, avoiding interference from low-quality loci. By adjusting the training of the hybrid deep learning model and optimizing hyperparameters through ten-fold cross-validation, a whole-genome selection model is obtained, shortening the breeding cycle and reducing the prediction error rate.

The application discloses a kind of aquatic habitat positioning prediction methods, it is related to aquatic habitat distribution research field.The present application establishes the aquatic habitat positioning framework of suitable area + suitable river reach by integrating the target biological distribution point and the environmental condition of river reach in target area.The fine prediction of aquatic habitat is realized in the multi-scale of area plus river reach, when facing the need of target biological habitat prediction in wide range area, fine positioning can be realized, so as to help fine management of aquatic organism, which has important guiding significance and practical significance for maintaining ecosystem stability.The common characteristics of suitable river reach are obtained by comparing after predicting suitable river reach, so as to identify and judge the potential habitat of target organism, the identification of potential habitat can expand the positioning range of target biological habitat, which can effectively avoid habitat omission and protection in time.

This invention provides a system and program that facilitates the process of generating and / or visualizing molecular networks for library molecular structure content. [Solution] In system 500, the molecular network generation system 502 for library molecular structure content includes an evaluation component 512 that performs a comparison between a first molecular fingerprint including first molecular structure data of a first molecular structure and a second molecular fingerprint including second molecular structure data of a second molecular structure, and a visualization component 516 that generates display data that visualizes a representation of the structural similarity score obtained from the first molecular structure, the second molecular structure and the comparison. The representation by the visualization component includes edges corresponding to the structural similarity score that extend between pairs of nodes corresponding to the first molecular structure and the second molecular structure.

The invention provides a Web-based single-cell RNA sequencing data intelligent analysis system and method. The method comprises the following steps: receiving a cell group through a Web interface; performing differential gene analysis on the single-cell RNA sequencing data contained in the cell group to obtain an original differential gene list, and filtering the original differential gene list by adopting a multi-threshold screening algorithm to obtain a target differential gene list; performing species automatic identification processing on the target differential gene list, calling a target local gene set database based on a result of the species automatic identification processing, and performing parallel enrichment analysis independent of network connection on the target differential gene list according to the target local gene set database to obtain a gene enrichment analysis result; and generating an interactive chart by adopting an intelligent label anti-overlapping algorithm so as to visualize the interactive chart. According to the method, the problems of high operation threshold, low batch analysis efficiency, unstable result and poor interactivity in the prior art are solved.

Embodiments of the present disclosure disclose an antigen-antibody binding conformation optimization method and device based on iterative closest point. A specific embodiment of the method comprises: obtaining a coarse-grained antibody file and a coarse-grained antigen file; performing surface residue extraction on the antigen structure file and the antibody structure file to obtain an antigen surface residue information set and an antibody surface residue information set; generating an antibody binding region residue list; generating an epitope model; performing spatial matching on the antigen surface residue information set and the epitope model to obtain a matched epitope model; generating an antibody docking conformation set; performing local optimization on each antibody docking conformation in the antibody docking conformation set to obtain an optimized antibody conformation set; and screening each optimized antibody conformation in the optimized antibody conformation set to obtain a target antigen-antibody binding conformation. The embodiment can improve the accuracy of the antibody conformation.

The invention discloses a path planning method for particles in a micro-fluidic chip of photoelectric tweezers, which comprises the following steps: acquiring image data in the micro-fluidic chip, identifying and classifying particles in an image in real time, and determining spatial position coordinates of each particle and position coordinates of a target area in real time; generating a corresponding virtual electrode based on the spatial position coordinate of each particle; setting a gravitational force source and a repulsive force source in the micro-fluidic chip, calculating a gravitational force vector of the virtual electrode, and calculating a total repulsive force vector of each repulsive force source borne by the spatial position of the current virtual electrode; synthesizing the gravitation vector and the total repulsive force vector into a total force vector, taking the direction of the total force vector as the movement direction of the target particle, and determining the movement rate of the target particle; and the next position of the target particle is predicted based on the motion direction and the movement speed of the target particle, the position of the target particle is updated through multiple iterations, and the virtual electrode drives the target particle to advance to the target area along the path with the minimum potential energy.

The invention discloses a protein and small molecule binding structure prediction method, device, equipment and medium, and relates to the technical field of protein and small molecule binding prediction.The method comprises the steps that a first modeling graph structure and a second modeling graph structure after a protein block and a to-be-tested small molecule compound are modeled respectively are obtained; the first modeling graph structure and the second modeling graph structure are input into a TANKBind model, and a predicted protein small molecule compound is output through the TANKBind model; generating a first topological file and a second topological file corresponding to the protein structure file and the small molecule structure file of the protein small molecule compound; performing molecular dynamics simulation by using a preset molecular dynamics simulation tool according to a simulation file generated by the first topological file and the second topological file to obtain a molecular dynamics simulation track file; and analyzing the molecular dynamics simulation track file, the first topology file and the second topology file to obtain a simulation analysis result.

A system may include an alignment algorithm and a sample deep learning model configured to output screening maps. The system may also include executable computing instructions that cause one or more processors to receive a plurality of experiment datasets comprising respective unaligned sample perturbation readouts from samples transfected with CRISPR-Cas9 reagents and respective control readouts from control samples transfected with intron targeting control reagents. The one or more processors may also identify intron feature characteristics for the control readouts using the alignment algorithm and generate aligned sample perturbation readouts from the unaligned sample perturbation readouts using the alignment algorithm and the intron feature characteristics identified. The one or more processors may also generate a screening map for the plurality of experiment datasets centered around the control readouts by processing the aligned sample perturbation readouts through the sample deep learning model.

The application provides a miRNA-disease relationship prediction method, system and model based on a hybrid expert model and a storage medium. The method comprises the following steps: obtaining a miRNA-disease correlation matrix of multi-omics data of a miRNA-disease to be predicted, a miRNA similarity matrix and a disease similarity matrix; constructing a miRNA-disease heterogeneous graph, a miRNA homogeneous subgraph and a disease homogeneous subgraph, and fusing them into a multi-modal biological graph network; inputting the fusion network into a pre-trained gated hybrid multi-expert network model to output a correlation probability of a miRNA-disease pair to be predicted; and the gated hybrid multi-expert network model comprises multiple expert networks, a gating network and an output layer. The application adopts a gating mechanism to adjust the weights of each expert for miRNA-disease prediction, maintains high prediction accuracy on a small-scale data set, and has the advantages of portability and light weight.

Time-series data formed by acquiring values indicating amounts or states of a plurality of biological materials for each biological material at a plurality of time points is prepared, the plurality of biological materials are divided into a plurality of groups on the basis of temporal variations of time-series data of the respective biological materials, representative time-series data indicating a state of each group is generated on the basis of time-series data of at least one biological material included in each group, and dependencies between the groups are estimated on the basis of the representative time-series data of each group.

The invention belongs to the technical field of genes, and particularly relates to a marker for identifying storage and preservation effects of strawberries as well as a screening method and application of the marker. The expression quantity of various cell wall degradation related genes in strawberry fruits treated by a storage method is measured, fruit preservation physiological indexes are combined, and comprehensive sorting is performed by homogenizing data through an assignment method; and analyzing the correlation between gene expression and assignment scores of fruit preservation physiological indexes in different treatments, and screening out genes with remarkable correlation as strawberry preservation markers. The marker disclosed by the invention can be used for quickly and accurately evaluating and predicting the quality and the fresh-keeping effect of the picked strawberries.

The application relates to the field of biotechnology and health informatics, and particularly discloses a microorganism marker combination for detecting obstructive sleep apnea of children and application thereof. The microorganism marker combination comprises the following nine microorganism genera: g__Streptococcus, g__Solobacterium, g__Schaalia, g__Capnocytophaga, g__Stomatobaculum, g__Mogibacterium, g__Clostridia_UCG-014, g__Selenomonas and g__Pseudoleptotrichia. By detecting the relative abundance of the above microorganism genera in a saliva sample of a child, a classification model is constructed by using a random forest algorithm, non-invasive and objective screening of OSA of the child is realized, and the application has high accuracy and clinical application value.

The application provides an explainable analysis method and system for burn prediction results, and belongs to the technical field of intelligent burn analysis. The method is to set a target result, continuously adjust biomarkers corresponding to the burn prediction result, obtain improved biomarkers, use a random forest model to predict the improved biomarkers to obtain improved results, continuously adjust the biomarkers corresponding to the burn prediction result until the improvement target is reached, and then explain and analyze the burn prediction result according to the improvement target. The technical problem that the existing technology cannot perform explainable analysis on the grading and typing prediction results by using the machine learning method is solved, a clinically understandable decision basis is formed, and systematic computing support is provided for burn diagnosis and treatment.

The invention relates to the technical field of medical treatment, and discloses a lung cancer personalized treatment scheme recommendation method and system and a storage medium. The method comprises the following steps: acquiring clinical and molecular indexes of a patient, and constructing a simplified feature set; distributing weights for treatment targets according to the simplified feature set, constructing and optimizing a scheme evaluation matrix, and generating a preliminary scheme sorting list; through threshold screening and patient feature matching degree verification, a verified scheme set is obtained; the schemes are classified based on gene mutation and driver gene features, and classified optimization scheme subsets are obtained; constructing an interaction model to analyze interaction influence of toxic and side effects and life quality on curative effects, and dynamically adjusting scheme scores; and if the score is lower than a threshold value, triggering iterative optimization, obtaining a scheme list after iteration, and further determining an optimal treatment scheme according to treatment collaboration and target balance. According to the method, intelligent and closed-loop optimization from multi-source data to personalized treatment decision is realized, and the personalization and accuracy of a treatment scheme are improved.