22 results about "Functional profiling" patented technology

The invention discloses an enterprise service platform intelligent management and control method and system based on function analysis, and relates to the technical field of platform intelligent management and control, and the method comprises the steps: collecting the calling frequency, resource occupancy rate, response time delay, user behavior track and abnormal event data of a function module, carrying out the standardization and time sequence verification, and constructing a multi-dimensional operation data flow. Constructing a functional behavior model based on a graph neural network, extracting module operation features and structural dependence, dynamically generating a functional thermodynamic scoring graph, and realizing visual display of module loads and abnormal hotspots; and constructing a functional twin copy for the key module, simulating a management and control strategy in an isolated simulation environment, and obtaining feedback. Constructing a multi-level management and control strategy generation matrix based on feedback, generating and optimizing candidate strategies, dynamically deploying, continuously monitoring and automatically adjusting; the system adopts a hierarchical rollback mechanism to realize strategy self-adaptive rollback in case of exception, so that stable and reliable operation of the platform is guaranteed.

The invention discloses a spatial transcriptome data analysis method based on artificial intelligence, and belongs to the technical field of spatial transcriptomics data analysis. Firstly, self-adaptive normalization and hypervariant gene screening preprocessing are carried out on original gene expression data; then constructing a hierarchical map integrating spatial proximity and transcription similarity, and ensuring the connectivity and robustness of the map through a dynamic radius pruning and neighborhood inheritance strategy; dividing positive and negative sample sets based on the atlas, and inputting a type modulation contrast graph auto-encoder for training; and finally, spatial domain identification and downstream function analysis are completed based on the low-dimensional potential representation or reconstructed gene expression matrix output by the model. The method effectively improves the accuracy and stability of spatial domain recognition, adapts to multi-technology-source data, enhances the biological interpretability of model output, and can be widely applied to biomedical scenes such as tumor microenvironment analysis and organ development research.

The invention relates to a method for functional analysis of a source system (1), wherein a syntax tree model (3) is generated from the program code data of the source system (1). In order to propose a method for functional analysis of a source system (1), in particular with extensive program code data, which method makes it possible to better identify interrelated functional units and, with high result quality and at the same time low resource requirements, to improve further processing by means of a large language model, the invention provides that a syntax analysis unit (4) generates a syntax embedding model by decomposing the syntax tree model (3) into individual syntax element embeddings (5), each representing a syntax element, and stores said syntax embedding model in a syntax embedding model memory (6), and that a language analysis unit (8) generates a language embedding model by decomposing the program code data into individual language element embeddings (9), each representing a language element, and stores said language embedding model in a language embedding model memory (10), whereupon, by means of a query unit (12), at least one query embedding (13) is generated from a query (11) representing a functional element of the source system (1) and those syntax element embeddings (5) and language element embeddings (9) which lie within a predefined range of similarity to the query embedding (13) are retrieved from the syntax embedding model memory (6) and the language embedding model memory (10), whereupon the syntax elements represented by the retrieved syntax element embeddings (5) and the language elements represented by the retrieved language element embeddings (9) are transferred as result elements (14) to an output unit (15), which generates therefrom a functional description dataset (16) and outputs the latter.

The present invention discloses a design failure mode and effect analysis method based on knowledge graph and large model retrieval enhancement, comprising the following steps: Step 1: Planning and preparation stage; Step 2: Structural analysis and functional analysis stage; Step 3: Failure analysis stage: Using graph knowledge reuse components to process input data and retrieve relevant information from the full-cycle knowledge base, the data is then processed and constructed by the industrial knowledge software system to obtain a knowledge graph; Based on the knowledge graph, the large model retrieval enhancement method deeply analyzes the input information, extracts relevant information from the knowledge graph in combination with the large model prompt learning method, and generates failure analysis results; Step 4: Risk analysis and optimization stage: Determine the issues that need to be addressed first, propose and implement improvement measures; Step 5: Result documentation and system integration stage. The present invention also discloses a design failure mode and effect analysis system based on knowledge graph and large model retrieval enhancement.

Disclosed herein are methods, compositions, systems, and kits related to functional testing of polypeptide-target interactions, such as antigen / immune receptor interactions, in a single-cell format.

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

The invention relates to the technical field of nuclear power, and provides a natural circulation flow analysis method, which comprises the following steps of: performing functional analysis on a main loop, identifying modeling objects with different functions, summarizing thermal-hydraulic common characteristics of the modeling objects, and establishing a unified local segment class. Through an object-oriented instantiation mechanism, a function model of each modeling object is quickly generated, and then a complete main loop system model is constructed. Automatic exchange of physical quantities such as pressure, flow, enthalpy value, temperature and heat flow among local sections is realized by adopting a connector mechanism, and interface logic does not need to be written manually. The method supports fusion of the thermal hydraulic model and the point reactor neutron dynamics model, unifies the simulation environment, remarkably improves the modeling efficiency, flexibility and model reusability, is suitable for main loop natural circulation flow calculation, design optimization, operation analysis and safety evaluation, and provides efficient and accurate modeling and simulation support for a nuclear reactor main loop.

The invention discloses a genome editing and detecting system for recognizing a DNA target based on TDP-43 dimerization and application of the genome editing and detecting system, and belongs to the technical field of molecular biology. FokT fusion protein construction, cell transfection and functional labeling, FokT-Seq detection through high-throughput sequencing, target annotation and functional analysis and disease mutation experience verification are carried out, and the genome editing and detecting system for recognizing the DNA target based on TDP-43 dimerization is obtained. Specific recognition and functional marking on the TDP-43 dimerization dependent DNA binding site are achieved for the first time, and traditional ChIP-Seq, CUTamp, CCTP-Seq, CCTP-Seq, CCTP-Seq, CCTP-Seq; a conformational state monomer or dimer when TDP-43 is combined with DNA cannot be judged by methods such as FOkI, FOkI is only activated in a dimerization state, and only a DNA target of TDP-43 combined in a dimer form is marked, so that the structure specificity of recognition is remarkably improved, and false positive binding site interference is avoided.

The invention belongs to the field of bioinformatics and computational biology, and provides a bioinformatics multi-omics association analysis method based on a co-disease model. Comprising transcriptome range correlation analysis based on GWAS and GTEx, causal analysis in a transcriptome range based on different Bayesian models, and screening and functional analysis of co-disease hub genes based on co-localization analysis and transcriptome data. According to the method, a complete technical system from macroscopic population queue genome analysis to human body multi-tissue fine positioning and then to microscopic cellular level gene target screening and verification is established, and a reliable target gene screening process and a mechanism analysis scheme are provided for precise prevention and treatment of metabolism and vasoplasmosis.

The invention belongs to the technical field of brain region function analysis, and particularly relates to a core brain region screening method and system. The core brain region screening method comprises the following steps: step 1, data acquisition and preprocessing; step 2, feature map construction and generation-discrimination network training; step 3, candidate sample generation, post-processing and quality evaluation; and 4, performing multi-algorithm feature consensus screening and stability evaluation. According to the core brain region screening method, firstly, class condition amplification with graph constraints is used, multi-index quality gating is assisted, then the reproducibility of feature selection is improved through multi-algorithm consensus screening and stability measurement, and finally, a stable and auditing judgment result is output in combination with multi-model integration. Therefore, the brain region screening performance and the generalization stability are remarkably improved under the scenes of scarcity of samples, class imbalance and high-dimensional structured data.

The invention relates to the technical field of bioinformatics, and discloses an MICP functional gene regulatory network analysis method, device and equipment and a medium. Expression data of an MICP functional gene is binarized and mapped into a one-dimensional ordered sequence; secondly, coding the gene state sequence into a quantum state vector, and performing efficient compression expression on the quantum state vector by using a matrix product state tensor network; on this basis, quantum mutual information between the gene pairs is calculated to construct a binary regulation skeleton network, and multi-element quantum mutual information is further calculated to identify a significant multi-gene synergy or redundancy regulation module; and finally, screening a reliable relationship through statistical test, integrating and constructing a comprehensive regulation and control network, performing function analysis, breaking through the bottleneck of a traditional method on high-order relationship capture through quantization and a tensor network, and remarkably improving the robustness and analysis depth of network inference.

The present application relates to the technical field of bioinformatics, and discloses a MICP functional gene regulation network analysis method, device, equipment and medium. The expression data of the MICP functional gene is binarized and mapped into a one-dimensional ordered sequence. Secondly, the gene state sequence is encoded into a quantum state vector, and a matrix product state tensor network is used for efficient compression representation. On this basis, not only the quantum mutual information between the genes is calculated to construct a binary regulation skeleton network, but also the multi-element quantum mutual information is further calculated to identify significant multi-gene synergistic or redundant regulation modules. Finally, reliable relationships are screened through statistical tests, and a comprehensive regulation network is integrated and constructed for function analysis. Through quantumization and tensor network, the bottleneck of traditional methods in high-order relationship capture is broken through, and the robustness and analysis depth of network inference are significantly improved.

The invention discloses a blood vessel structure and function coupling analysis method and system based on multi-mode MRI, and belongs to the field of medical image analysis and computational physiology. According to the method, 4D Flow MRI hemodynamic data and blood vessel spatial configuration features are fused, and the problem that in the prior art, structure and function analysis is split, and the coupling relation between the structure and the function cannot be revealed is solved. The method comprises the core steps of blood vessel image acquisition and preprocessing, structural feature extraction, functional parameter calculation, coupling analysis and result output, and the system correspondingly comprises functional modules. According to the method, automatic registration and collaborative analysis of structural-functional data are realized, comprehensive support can be provided for diagnosis of diseases such as liver cirrhosis and portal hypertension and TIPS surgical planning, and clinical prediction accuracy and diagnosis and treatment efficiency are improved.

This invention discloses a functional analysis system for skeletal muscle organoids and its applications. The core of the system lies in its use of computer vision technology to automatically analyze motion videos of calcium-ion fluorescently labeled skeletal muscle organoids under stimulation. The system automatically identifies organoid regions of interest (ROIs) through specific image processing algorithms (such as adaptive threshold fusion and contour detection), extracts time-series data of calcium signal intensity, and calculates characteristic parameters such as relative fluorescence change rate and peak frequency, ultimately outputting a quantitative functional assessment report. This assessment scheme is low-cost, high-throughput, objective, and accurate, overcoming the technical bottleneck of existing organoid functional assessments that rely on subjective manual analysis or expensive specialized equipment. It is compatible with various culture systems, greatly promoting the large-scale application of skeletal muscle organoids in drug screening and toxicity testing.

The invention relates to the technical field of biology, and discloses an application of a millet KQL12033 gene in a seed development process. The Setaria italica KQL12033 gene belongs to an ALOG gene family, and the Setaria italica KQL12033 gene has a nucleotide sequence as shown in SEQ ID NO: 1. A millet ALOG gene family is screened and identified through a bioinformatics technology, the expression quantity trend of ALOG gene family members in the seed development process is further analyzed in combination with a high-throughput sequencing technology, and it is determined that the KQL12033 gene participates in the millet seed development process. The invention lays a foundation for large-scale identification and functional analysis of seed development related genes.

Disclosed is an analytical platform for profiling and functional analysis of proteomes, which can unveil new biological insights potentially useful for instigating novel therapeutic interventions. The disclosed analytical platform for proteome profiling is constructed by integrating plasmonic biosensor microarrays with tandem-ion mobility spectrometry. The disclosed analytical platform can separate, identify, and characterize highly similar proteoforms as well as low abundance proteoforms from plasma or other samples.

Disclosed is an analytical platform for profiling and functional analysis of proteomes, which can unveil new biological insights potentially useful for instigating novel therapeutic interventions. The disclosed analytical platform for proteome profiling is constructed by integrating plasmonic biosensor microarrays with tandem-ion mobility spectrometry. The disclosed analytical platform can separate, identify, and characterize highly similar proteoforms as well as low abundance proteoforms from plasma or other samples.

The present application belongs to the technical field of bio-information analysis, and provides a method for identifying lineage-specific expanded gene families. The present application is a method for screening and identifying lineage-specific expanded genes in different subfamilies by using orthology analysis. The method is simple to operate, requires a small amount of data, and the analysis software is easy to learn to operate. The screened genes are well representative, which lays a foundation for future large-scale identification and functional analysis of lineage-specific genes.

1. The name of the product under this design: Coagulation Analyzer (UG500). 2. Purpose of the product with this design: used for functional analysis of coagulation, anticoagulation, etc. on whole blood samples and for medical clinical diagnosis. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

The invention discloses an electromechanical system architecture forward design method based on a servitization thought, and belongs to the field of airplane electromechanical system design. The connection of requirements, functions, services, logic and physical architectures is realized through model-based system engineering (MBSE). Specifically, the method comprises the following steps of: establishing a demand structured model by using a SysML language, analyzing a target system function to obtain a service scheme meeting a function demand, realizing service logic through a logic layer state machine, and performing joint simulation with a physical layer simulation model to form'demand-function-service-logic-physics' full-link mapping taking the model as a clue; and the forward design of the system architecture is realized.

The present disclosure provides, in various aspects and embodiments, methods for assessing the presence or absence of colorectal neoplasms, such as colorectal cancer (CRC), colorectal adenoma (CRA), and advanced colorectal adenoma (CRAA), in a subject by metagenomic and multiomic analysis of faeces or other biological samples. In other aspects, the disclosure provides methods for generating machine learning models or "profiles" based on metagenomic and multi-omics analysis of faeces or other biological samples to evaluate the presence or absence of colon conditions, including but not limited to CRC, CRA, and CRAA, in a subject.

The present invention provides a multi-dimensional microservice splitting method and system for power grid business, which relates to the field of power grid information technology. Starting from the perspective of multi-dimensional data, the present invention introduces three dimensions of business, function and pressure, and conducts a comprehensive analysis of each business module in the power grid business monomer program. The present invention designs a multi-channel graph neural network to process the multi-attribute graph to achieve splitting, wherein trainable weights are introduced in the graph convolution coding part to improve the quality of graph convolution feature extraction, a gating mechanism and a cross-channel residual connection are used in the feature fusion part to obtain a high-quality comprehensive feature representation, and trainable weights are introduced in the node clustering part to improve the clustering effect. Compared with the traditional method that relies only on functional analysis, the present invention can more accurately identify the intrinsic connections and potential dependencies between modules, improve the splitting accuracy and rationality, and avoid errors caused by manual experience.