660results about "Data visualisation" patented technology

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 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 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 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 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 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.

The invention discloses a peanut quality character selective breeding method based on whole genome SNP and application, and relates to the technical field of crop breeding, the method comprises the specific steps of data acquisition, SNP optimization and marker screening, model construction and new strain breeding; by integrating whole genome re-sequencing, SNP optimization marker screening and mixed deep learning model construction, peanut quality character prediction and breeding value evaluation are achieved, through whole genome re-sequencing and quality control processes, an SNP variation map is obtained, ten key quality characters including protein, oil content, oleic acid and the like are covered, and the breeding value of peanuts is evaluated. According to the method, comprehensive genetic information is provided for subsequent analysis, a feature marker set is formed by screening out sites in the SNP optimization and marker screening link, interference of low-quality sites is avoided, a whole genome selection model is obtained by adjusting and training a mixed deep learning model and optimizing hyper-parameters through ten-fold cross validation, the breeding period is shortened, and the breeding efficiency is improved. And the prediction error rate is reduced.

The invention provides a zero-code biological information analysis method and device based on multi-agent collaboration, and the method comprises the steps: setting a large language model as a plurality of agents through cue words, carrying out the scoring and rearrangement of tools in a tool library through the plurality of agents, and finding an optimal matching tool through discussion; exploring type assembly is carried out by utilizing intelligent agents of a plurality of explorers according to input and output parameters of different tools, fusion is carried out after multiple rounds of tests are successful, a workflow is established, the workflow is written into codes, and the codes are executed to obtain an analysis result; checking and screening the analysis result to obtain screening information; presenting the screening information in a graphic mode to obtain graphic information; and obtaining candidate answers of the to-be-analyzed task according to the screening information and the graphic information.

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 provides a gene module analysis method and device, equipment and a storage medium, and relates to the technical field of space transcriptomics. The method comprises the steps of obtaining space-time group chip expression data, performing linearization processing, and performing sliding window cutting to obtain a plurality of window units arranged according to a space sequence; calculating gene function activeness and constructing a matrix; clustering and grouping are carried out to obtain window groups; performing difference analysis screening on the window groups to obtain a difference expression gene set; and performing gene module analysis to obtain a functional gene module. According to the method, through an integrated processing flow of structure linearization, sliding window cutting, activeness quantification, clustering analysis and module identification, fine extraction and function reconstruction of spatial expression information in the curled tissue are realized under the condition that an additional algorithm or external data is not introduced; and the practicability and research value of the spatial omics data in a complex organization structure are improved.

The invention discloses a differential gene regulation and control network reconstruction method based on mutual information and a redundancy regulation and control filtering system. The method comprises the following steps: firstly, collecting gene expression data under two or more conditions, respectively calculating mutual information of gene pairs under each condition, and screening the gene pairs with obvious mutual information difference as candidate regulation edges; furthermore, a redundancy regulation and control relation caused by the intermediary variables is identified and eliminated through calculation condition mutual information, and finally a difference regulation and control network with high credibility is constructed. The system comprises a data preprocessing module, a mutual information calculation module, a redundancy indirect regulation and control effect filtering module, a regulation and control direction judgment module, a network construction and threshold optimization module, a difference network construction module and an output and visualization module. The method can effectively improve the biological interpretability and inference accuracy of the network, and is widely applied to the bioinformatics fields such as disease mechanism research, regulatory factor identification and multi-omics integrated analysis.

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 chip for genome breeding and variety identification of tilapia mossambica. The invention provides a group of SNP (Single Nucleotide Polymorphism) marker combination for tilapia mossambica, which comprises 50000 SNP markers which are respectively SNP1-SNP50000 markers. According to the invention, the SNP marker combination related to economic characters of tilapia mossambica is integrated, representative sites with uniform coverage are selected, a biological probe for site typing is developed, and the site coverage, typing accuracy and GS accuracy of the SNP marker combination are basically consistent with those of re-sequencing; the method can be applied to the aspects of tilapia germplasm resource identification, genetic relationship identification, SNP typing, variety identification, molecular breeding, DNA fingerprint database construction, variety purity detection, germplasm resource genetic analysis, whole genome selective breeding, functional gene positioning, genetic map construction, genetic evolution analysis, whole genome association analysis and the like.

In some aspects, the present disclosure provides a method for generating a geometrical structure of a binding complex formed between a protein and a ligand. In some embodiments, the method comprises sampling an initial geometrical structure of the binding complex from a geometry prior. In some embodiments, the method comprises denoising, using a machine-learned stochastic differential equation (SDE), the initial geometrical structure to generate the geometrical structure of the binding complex.

The invention relates to the technical field of biomedicine, in particular to a liver cancer molecular subtype typing and prognosis model construction method based on glycometabolism and lactic acid metabolism related genes and application thereof. According to the invention, the typing and prognosis model of liver cancer subtypes is constructed for the first time on the basis of a coordinated regulation network of a glycometabolism gene and a lactic acid metabolism gene, the constructed liver cancer subtype subtypes comprise Cluster1 and Cluster2, and the total survival rates of different subtypes have significant difference; the clinical characteristics T.stage and TNM.stage of different subtypes are obviously different from each other; 12 kinds of immune cells have significant differences among different subtypes; iPS immune scores of different subtypes are significantly different; the TIDE scores of different subtypes have significant score differences; the genes with the highest mutation frequency in different subtypes comprise TP53, CTNNB1 and TTN. CLM scores of the prognosis model constructed on the basis of the collaborative regulation network of the glycometabolism genes and the lactic acid metabolism genes have no significant difference in different patient states; the 17 immune cells have significant differences between high and low risk groups. And a new way is provided for individualized treatment strategy formulation and survival outcome improvement of HCC.

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.

The invention relates to the technical field of bioinformatics, in particular to a method for typing acute respiratory distress syndrome biological subtypes. The method comprises the following steps: a) acquiring multi-omics data and carrying out standardized preprocessing; the multi-omics data comprises transcriptomics data, proteomics data and metabonomics data of a biological sample source; b) constructing a similarity network of each group by using a similarity fusion network (SNF), and obtaining a uniform sample similarity matrix through multi-group network fusion and iteration; multiple collaborative principal component analysis (MCIA) is adopted to carry out dimension reduction on multi-omics data so as to realize visualization of a clustering result; carrying out multi-omics joint discrimination modeling under the guidance of SNF clustering by using a data integration analysis (DIABLO) method so as to identify key feature variables; and carrying out biological subtype classification based on the clustering result of the steps.

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.

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.

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 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 provides a multi-element machine learning model-based cross-species lung disease characteristic gene screening method and system, an electronic system and a storage device. The method comprises the following steps of: acquiring single cell / transcriptome data related to mouse lung diseases from a public database and preprocessing the single cell / transcriptome data; training the model by adopting six machine learning algorithms and outputting a gene importance score; calculating the weight according to the model performance and normalizing the score; and integrating the cross-species scores through a weighted fusion formula, and outputting a feature gene list and a visual report. The system comprises a data acquisition and preprocessing module, a multi-element machine learning model training module, a weight calculation and normalization module, a cross-species comprehensive scoring module and a result output module. The screening accuracy, stability and generalization ability are improved through multi-algorithm integration and cross-species fusion, and the method can be widely applied to the fields of mechanism research of lung diseases, diagnosis marker development and drug target verification.

The invention relates to the field of synthetic biology, in particular to a high-flux biosynthesis ink-jet printing control method and device, and the method comprises the steps: sequentially extracting base data from an obtained DNA base sequence file, and forming a one-dimensional original array; according to a preset printing width, the one-dimensional original array is converted into a two-dimensional array, meanwhile, visualization processing is carried out, corresponding colors are given to different basic groups, and a basic group distribution array diagram with matched row and column structures is obtained; establishing null arrays which correspond to different bases and are consistent in dimension based on the null arrays, traversing the original arrays and assigning values to the corresponding bases at the corresponding positions of the null arrays to obtain a plurality of single base arrays; converting the arrays into a two-dimensional array according to a preset width, and then converting the two-dimensional array into a monochromatic bitmap; and sending the bitmap according to the corresponding relationship between the basic groups and the nozzles and controlling printing. According to the invention, efficient and accurate printing of the base sequence in high-throughput biosynthesis is realized, and the synthesis efficiency and reliability are improved.

The invention discloses a dental plaque visual detection system based on micro-ecological recognition and trend prediction. The dental plaque visual detection system comprises a data acquisition module, a prediction modeling module, a network analysis module and a risk quantification module. According to the system, a miniaturized oral cavity sensing array and a high-throughput micro-fluidic chip are used for collecting the relative abundance of dental plaque flora and dynamic characteristics such as oral cavity acidification, buffering and remineralization, and future flora data are generated through a depth time sequence prediction model. A flora interaction network is constructed by combining spatial co-localization and causal inference, kinetic parameters such as node centrality, edge weight and promotion and suppression polarity are extracted by using a graph neural network, components such as pathogen expansion amount, antagonism decline amount, network modulation amount and protection amount are calculated, and the components are fused into a dynamic pathogenic risk index (D-PRI). The method has the beneficial effects that the index can be visually presented on a time axis, personalized intervention suggestions are generated, and quantitative prediction and accurate early warning of the dental plaque risk are realized.

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 provides a method for analyzing the quality of a microbial fermentation product based on a Raman spectrum and a neural network algorithm. The method comprises the following steps: step 1, collecting Raman spectrum data of the microbial fermentation product; 2, vectorizing the Raman spectrum data to obtain a Raman spectrum feature vector; and step 3, inputting the Raman spectrum feature vector into a fermentation product quality prediction model for forward propagation so as to predict the quality of the microbial fermentation product. According to the technical scheme, an efficient, accurate and lossless intelligent quality control solution is provided for the microbial fermentation industry.

The invention discloses a space transcriptome and space metabolome integration method based on deep learning. The method comprises the following steps: firstly, acquiring original space transcriptomics data and original space metabonomics data of a biological tissue, and performing data preprocessing to obtain a preprocessed biological tissue data set; then, aligning space metabonomics data in the preprocessed biological tissue data set to space transcriptomics data, unifying data resolution, obtaining corrected space metabonomics data, and updating the biological tissue data set; and finally, generating to-be-integrated sample data from the newest biological tissue data set, inputting the to-be-integrated sample data into the spatial multi-omics data integration model, and outputting final joint embedding by the model, so that cross-modal and cross-sample effective integration of the data can be realized. According to the method, the problems of form and resolution inconsistency and batch effect caused by technical difference of ST and SM data are solved, and high-precision and interpretable spatial multi-omics integration analysis is realized.

The invention relates to a protein pocket detection system and method and a storage medium, and the detection system comprises a conformation generation module, a pocket detection module, a task scheduling and management module and a visualization module: the task scheduling and management module obtains to-be-detected protein data; the conformation generation module is used for generating a conformation track of the protein data by utilizing molecular dynamics simulation based on the protein data acquired by the task scheduling and management module; the pocket detection module is used for determining pocket data based on the conformation track generated by the conformation generation module; wherein the pocket data is used for representing the stable data of the spatial position and the geometric shape of the protein data in the change process of the conformation trajectory; and the visualization module is used for rendering and outputting the conformation track generated by the conformation generation module and the pocket data determined by the pocket detection module. The problem that in the prior art, key drug targets generated in the dynamic process of protein cannot be efficiently and conveniently found is solved.