16 results about "Functional similarity" patented technology

This invention relates to the field of Internet of Things (IoT) technology and is a cascading threat detection method for automated rules in IoT. It utilizes a large language model to parse rule text, extract semantic elements, and construct a semantic representation model. A semantic alignment model is built to measure the functional similarity of different rules in a rule set. A TAP rule heterogeneous graph is constructed, establishing explicit and implicit relationships respectively. A dual-attention context encoder is constructed to obtain rule embedding representations containing semantic dependency information. A global relation attention mechanism is introduced to measure the importance of explicit and implicit relation spaces under different node types. The invention determines whether cascading threat paths exist in the rule set and generates threat detection results. This invention can automatically parse rule semantics, construct explicit and implicit dual-relation graph models, and utilize multi-layer graph neural networks to achieve multi-hop inference, thereby comprehensively detecting potential rule-cascading threats.

The application relates to a cell infiltration inference method and system fusing GO function annotation and PPI network information, and the method comprises the following steps: collecting gene expression data, GO function annotation data and PPI network data; constructing a cell-cell function correlation network and a cell-cell physical interaction network respectively; performing weighted fusion processing on the two networks to obtain a comprehensive cell relationship network; calculating a final cell infiltration score through a restart walk algorithm, and inferring the infiltration degree in a tumor microenvironment according to the final cell infiltration score. The application innovatively fuses GO function annotation information and PPI network data, comprehensively considers the functional similarity and physical or signal interaction between cells, enables the model to understand cell synergy from the biological pathway level and analyze cell direct interaction from the protein interaction level, avoids one-sidedness of a single perspective, and provides a more stereoscopic cognitive framework for tumor microenvironment analysis.

The invention relates to a cell infiltration inference method and system fusing GO function annotation and PPI network information. The method comprises the following steps: collecting gene expression data, GO function annotation data and PPI network data; respectively constructing a cell * cell function association network and a cell * cell physical interaction network; carrying out weighted fusion processing on the two to obtain a comprehensive cell relation network; and calculating a final cell infiltration fraction through a restart migration algorithm, and deducing the infiltration degree in the tumor microenvironment according to the final cell infiltration fraction. According to the method, GO function annotation information and PPI network data are creatively fused, functional similarity and physical or signal interaction between cells are comprehensively considered, the model can understand cell synergy from the biological pathway level and can analyze cell direct interaction from the protein interaction level, one-sidedness of a single view angle is avoided, and the method has the advantages of being simple in structure and convenient to operate. And a more three-dimensional cognitive framework is provided for tumor microenvironment analysis.

The present application discloses a method for annotating arbitrary code snippets and a retrieval system thereof, which relates to the field of machine learning. The method comprises: establishing a code snippet database, activating a multidimensional feature annotation model, performing code snippet annotation, and establishing a first annotation result; calling an adaptive annotation network, utilizing the adaptive annotation network to perform contextual backtracking analysis on the code snippets in the database, and establishing a second annotation result; performing static and dynamic fusion analysis on the database, establishing joint functional similarity, and establishing a third annotation result; performing code version change annotation, and establishing a fourth annotation result; and performing code annotation retrieval management based on the four annotation results. The method solves the technical problem of low management efficiency and retrieval accuracy of code snippets due to the high complexity of code snippets in multidimensional feature annotation, and achieves the technical effect of improving the annotation accuracy and retrieval efficiency of code snippets by combining multidimensional annotation with adaptive backtracking analysis.

The application belongs to the technical field of picture processing, and particularly relates to a picture generation method based on mutation data, a generation system and a cancer metastasis prediction method. Step one: constructing a pathway image by using the functional similarity of pathways on a gene interaction network; step two: constructing a patient characteristic image; the prediction method further comprises: training a prediction model by using the constructed patient characteristic image, and predicting the metastasis of mutation data by using the trained prediction model. The application aims to solve the problems of lacking high-performance generation of patient characteristic pictures based on single nucleotide variation and the technical problems of cancer metastasis prediction and determination.

The invention belongs to the technical field of enzyme catalytic conversion number prediction, and discloses an enzyme catalytic conversion number prediction method and system based on function annotation and a hierarchical structure, and the prediction method comprises the steps: extracting protein unique identifier information based on a protein resource database, analyzing protein dynamic information and gene ontology functions according to the unique identifier information, and obtaining a prediction result; obtaining a relation table of the gene ontology and the enzymatic conversion coefficient; combining the hierarchical structure of the gene ontology with the relation table, capturing the mutual relation between the gene and the gene product, and constructing a hierarchical total tree between the gene ontology and the enzymatic conversion coefficient according to a relation capturing result; and extracting target gene ontology information according to the gene code of the target object, and matching an enzymatic conversion coefficient corresponding to the target gene ontology information from the hierarchical total tree as an enzymatic conversion number prediction result. According to the invention, gene and protein function annotations are provided by using the gene ontology, so that the function similarity of enzymes can be measured based on the gene ontology, and the enzymatic conversion value of unknown enzymes is speculated.

The present invention relates to the field of bioinformatics association prediction technology, and specifically to an encoder-based gradient boosting machine miRNA-disease association prediction method. The method comprises: utilizing multi-source biological and medical information to obtain a miRNA-disease association adjacency matrix, a miRNA functional similarity matrix, and a disease semantic similarity matrix; concatenating the resulting integrated miRNA-disease similarity matrix with the miRNA-disease association adjacency matrix to obtain a more informative miRNA-disease association feature vector; utilizing an autoencoder to extract key features of the combined miRNA feature vector and disease feature vector, and utilizing a lightweight gradient boosting machine classifier to predict potential associations between miRNAs and diseases. The present invention achieves high prediction accuracy with low time and economic costs, aims to uncover potential miRNA-disease associations, and can aid in the study of the pathogenesis of complex diseases.

The present disclosure provides an instruction matching method and apparatus, a wearable device, and a storage medium, which belong to the field of instruction matching technology. The method includes: determining an instruction list based on a function type and an interactive action type. Determining a function priority sequence corresponding to a function type and an action priority sequence corresponding to an interactive action type based on historical usage data. Determining the functional similarity of multiple functions in a function priority sequence based on the function type, and determining a function-action matching relationship based on the function priority sequence, the action priority sequence, and the functional similarity. Determining an instruction matching relationship based on the function-action matching relationship and the instruction list. The instruction matching relationship includes an action-instruction-function matching relationship. The instruction matching method and apparatus, wearable device, and storage medium provided by the present disclosure can solve the problem that the prior art lacks an effective instruction matching method, and improve the user experience.

Quantification and Detection of Immune Landscape Changes An immune response to an event is detected and quantified by accessing sets of immune cell sequences taken from a sample before and after an event, detecting the immune cell sequences showing a significant change between before and after the event, clustering the immune cell sequences so as to cluster together cell sequences having functional similarity, selecting the clusters containing at least one immune cell sequence showing a significant change and calculating an immune response score based on the selected clusters.

The invention discloses an AI-assisted test vector analysis and optimization method and system, and relates to the technical field of integrated circuit design automation. The method comprises the following steps: a feature extraction step: obtaining a test vector set and extracting multi-dimensional features including fault coverage fingerprints and a scan chain load mode, and constructing a feature matrix; an intelligent clustering step: clustering the test vector set by using an unsupervised clustering algorithm, and dividing vectors with similar functions into the same vector cluster; a representative vector selection step: selecting representative vectors in each vector cluster based on a fault coverage integrity principle to form an optimized vector set; and an iterative optimization step: verifying the optimized vector set, and if a preset condition is not met, dynamically adjusting the clustering parameter and repeating the previous steps. According to the method, deep compression of the test vector is realized through global function similarity analysis, the test time and the test cost can be remarkably reduced, and the method has the advantages of full-automatic post-processing, support of multi-objective optimization and the like.

The invention relates to a city charging prediction method and device based on a multi-semantic topological graph and a medium, and the method comprises the steps: dividing a target city region into a plurality of space nodes, obtaining the space information and semantic information of each space node, and constructing a geographic adjacency matrix and a plurality of single semantic similarity matrixes; constructing a long-range connection candidate set based on the single semantic similarity matrix; obtaining a scene demand, and performing sparsification on the long-range connection candidate set based on the scene demand to obtain a long-range shortcut matrix; constructing a multi-semantic topological graph of the target area based on the geographic adjacency matrix and the long-range shortcut matrix; historical city charging data is acquired, and the future city charging demand is predicted by using the space-time diagram neural network based on the multi-semantic topological graph and the historical city charging data. Compared with the prior art, the method has the advantages that a traditional topological graph is optimized by fusing multiple spatial semantics such as geographical adjacency, functional similarity, travel modes and behavior modes, and therefore more accurate charging prediction is achieved.

Disclosed is a cloud-native application programming interface (API) recommendation method fusing data augmentation and contrastive learning. Service information is included on the basis of a service information double-graph structure, and a mutual attention mechanism is designed to compute an importance degree of each layer of information. A data optimization method for sequence information based on functional similarity and a computation method for similarity between services based on two parts of information are provided; on this basis, data of a service invocation sequence is augmented with the idea of contrastive learning to form an augmented sequence pair; a computational contrastive loss function is combined with a pair-wise recommendation loss function to optimize an overall model, thereby improving the effect of a service recommendation model; and according to a feature embedding representation result of a service, pair-wise recommendation scores are computed to complete service recommendation.

The invention relates to the technical field of machine learning, and discloses an miRNA subcellular localization prediction method and system based on hypergraph and contrast learning. The method comprises the following steps: extracting miRNA-disease associated characteristics and miRNA-mRNA associated network characteristics of a miRNA function similarity network, a miRNA-mRNA associated network and a miRNA-disease associated network by adopting a constructed hypergraph; carrying out comparative learning fusion processing on the miRNA sequence features, the miRNA-disease associated features and the miRNA-mRNA associated network features to obtain comparative learning fusion features; and establishing a miRNA subcellular localization prediction model according to the contrast learning fusion features, and carrying out localization based on the miRNA subcellular localization prediction model. The correlation between miRNA and mRNA and diseases is fully utilized, and positive and negative samples are compared by contrast learning, so that the accuracy of miRNA subcellular localization prediction is effectively improved.

The present invention provides a lncRNA-disease association prediction method based on a weighted kernel canonical regularization algorithm, comprising the following steps: S1: obtaining the lncRNA-disease adjacency matrix LD; S2: calculating the lncRNA expression similarity LS exp , lncRNA functional similarity LS fun , lncRNA Gaussian similarity LS gau , lncRNA linear neighborhood similarity LS lin , disease semantic similarity DS sem , disease Gaussian similarity DS gau , disease linear neighborhood similarity DS lin S3: The k-nearest-neighbor centered kernel alignment algorithm is used to integrate the similarities of lncRNAs and diseases into the same space. S4: A heterogeneous matrix is ​​constructed using the lncRNA-disease association matrix and the optimal similarity kernel matrix of lncRNAs and diseases as the target matrix for matrix completion. S5: The heterogeneous matrix is ​​input into a weighted kernel canonical regularization model for completion, ultimately obtaining the predicted lncRNA-disease association. This method utilizes the k-nearest-neighbor centered kernel alignment algorithm to integrate similarity information for auxiliary prediction and constructs a weighted kernel canonical regularization model to complete the heterogeneous matrix, achieving more accurate lncRNA-disease association prediction.

The invention provides a miRNA-lncRNA interaction prediction system based on multi-view projection fusion and truncated matrix decomposition, and belongs to the crossing field of bioinformatics and machine learning. The method comprises the following steps: firstly, constructing a comprehensive miRNA-lncRNA similarity map by comprehensively utilizing sequence similarity, functional similarity, expression similarity and Gaussian kernel similarity of miRNA / lncRNA, and constructing a corresponding network structure based on different similarities and known interaction information; secondly, a multi-view projection fusion method is provided, similar network topology structures from multiple view angles and original interaction matrix information are fully fused, and abundant topology features in the biological network are mined while the integrity of the interaction matrix structure is maintained. Finally, a truncated matrix decomposition technology is introduced, matrix dimension reduction is achieved through the number of truncated singular values, key feature information is reserved, and therefore prediction efficiency and precision are improved.

The invention relates to the technical field of Internet of Things, in particular to an Internet of Things automation rule-oriented cascade threat detection method, which comprises the following steps of: analyzing a rule text by using a large language model, extracting semantic elements and constructing a semantic representation model; constructing a semantic alignment model, and measuring functional similarities of different rules in the rule set; constructing a TAP rule heterogeneous graph, and respectively establishing an explicit relationship and an implicit relationship; constructing a double-attention context encoder to obtain a rule embedding representation containing semantic dependency information; introducing a global relation attention mechanism, and measuring the importance of an explicit relation space and an implicit relation space under different node types; and judging whether a cascade threat path exists in the rule set, and generating a threat detection result. According to the method, rule semantics can be automatically analyzed, the explicit and implicit double-relation graph model is constructed, and multi-hop reasoning is realized by using the multi-layer graph neural network, so that potential rule cascade threats are comprehensively detected.