60 results about "Functional similarity" patented technology

A system and method for matching one or more source schemas with one or more target schemas is provided. The matching between source and target schemas is performed by gathering inputs pertaining to the source and target schemas, wherein the inputs comprises a set of details in a predefined format. Thereafter, the gathered inputs are processed by comparing the source schemas with the target schemas. The processing is performed to identify a set of matches between the source and target schemas based on the linguistic similarity, structural similarity and functional similarity and relationship between the source and target schemas. Subsequently, the identified matches are stored.

The invention discloses a construction method for a specific cancer differential expression gene regulation and control network.The method includes the following steps of firstly, constructing a framework gene interaction network according to function similarity weight numbers between genes; secondly, conducting module division on the framework gene interaction network through a segmenting method; thirdly, screening out differential expression genes through complete genome methylation data; fourthly, classifying the screened-out differential expression genes according to functions; fifthly, using all the differential expression genes mapped to each same function module as a function classification; sixthly, constructing the regulation and control network of all the genes in each function classification function; seventhly, conducting sub-network splicing under guidance of a framework network.The calculation complexity is greatly reduced, and high precision is achieved.

The invention belongs to the field of a biotechnology, and provides a comparison method based on a function module and used for biomolecular networks such as a genetic expression regulatory network or protein-protein interaction and the like. The method mainly comprises steps as follows: an adjacency matrix Madj of a biological network is built, a function similarity matrix Msim between network nodes is calculated, a function weight matrix ME of a network side is calculated according to the formula as follows: ME=Madj.*Msim, a network module is mined with a minimum graph entropy algorithm, and finally, function enrichment analysis is performed on the network module, wherein the symbols are defined in the instruction.

The invention provides a method for blocking SARS coronavirus to infect cell with polypeptide or its derivative. The cistine series of polypeptide comes from two heptad repeat districts HR1 and HR2 of SARS coronavirus spike(S) protein. The invention also provides a method for producing the functional similarity with HR1 and HR2. the invention provides a method for producing HR polypeptide and its derivant with coalescence expression method. All the peptides have rejection capability to SARS.

A system and method for matching one or more source schemas with one or more target schemas is provided. The matching between source and target schemas is performed by gathering inputs pertaining to the source and target schemas, wherein the inputs comprises a set of details in a predefined format. Thereafter, the gathered inputs are processed by comparing the source schemas with the target schemas. The processing is performed to identify a set of matches between the source and target schemas based on the linguistic similarity, structural similarity and functional similarity and relationship between the source and target schemas. Subsequently, the identified matches are stored.

A method and a system for website navigation path analysis are disclosed. The system comprises of a processor; a non-transitory computer-readable storage medium coupled to the processor. The processor executes a plurality of the modules / subsystems stored in the storage medium such as a data categorizing module for categorizing web pages of the website into one or more groups of web pages based on the domain knowledge and functional similarities between the web pages; a score assigning module for calculating an index score, casual score, base line score and engagement score for web page elements and categorized web page pair; a statistical model to be trained with the scores and weight calculated by the score assigning module, and an analyzing module for determining which web pages and transitions correspond to engagement and decision making based on the trained statistical model.

The embodiment of the invention discloses a protein function prediction method and a protein function prediction device. The method comprises the following steps of obtaining amino acid sequence characteristics of a target protein and amino acid sequence characteristics of a reference protein; based on the amino acid sequence characteristics of the target protein and the amino acid sequence characteristics of the reference protein, judging whether the functions of the target protein and the reference protein meet a similarity judgment rule or not; and if yes, determining the function of the target protein according to the function of the reference protein. The similarity between the function of the target protein and the function of the reference protein is judged from the deep feature level of the amino acid sequence related to the function, so that the influence of the ambiguity of the surface feature of the amino acid sequence on the function similarity judgment can be avoided. Thecoverage rate of protein function prediction is improved.

The invention discloses multidata integration circular RNA and a disease correlation prediction method based on double random walk restart. By transforming a circular RNA-disease relationship networkinto an undirected graph, calculating the semantic similarity of circular RNA function annotations, structural similarity and functional similarity, and calculating the disease function and semantic similarity, multiple circular RNA similarity networks and disease similarity networks are integrated into a comprehensive circular RNA similarity network and disease similarity network, a random walk restart algorithm is applied to the integrated circular RNA similarity network and the disease similarity network separately, the cold restart problem is avoided, and a potential circular RNA-disease relationship is predicted. Accordingly, the potential circular RNA-disease relationship can be accurately predicted; simulation experiment results indicate that the precision, recall rate, accuracy, f1-measure and other indexes are better; compared with other relationship prediction methods, the prediction accuracy of the RNA-disease relationship is improved.

A computer implemented method of estimating a similarity of binary records comprising executable code, comprising converting a first binary record and a second binary record to a first intermediate representation (IR) and a second IR respectively, decomposing each of the first IR and the second IR to a plurality of strands which are partial dependent chains of program instructions, calculating a probability score for each of the plurality of strands of the first IR to have an equivalent counterpart in the second IR by comparing each strand of the first IR to one or more strands of the second IR, adjusting the probability score for each strand according to a significance value calculated for each strand and calculating a similarity score defining a functional similarity between the first IR and the second IR by aggregating the adjusted probability score of the plurality of strands.

The invention discloses a method for predicting disease-related metabolites by adopting double random walk. The method comprises the following steps: firstly, calculating semantic similarity of a disease by utilizing the semantic information of the disease, and calculating functional similarity of the metabolites according to the relationship between the disease and the metabolites; secondly, according to the known metabolite-disease relationship, calculating Gaussian kernel action spectrum similarity of the disease and the metabolites respectively; thirdly, constructing a final disease similarity network based on the semantic similarity of the disease and the Gaussian kernel action spectrum similarity of the disease, and constructing a final metabolite similarity network based on the function similarity of the metabolites and the Gaussian kernel action spectrum similarity of the metabolites; and finally, constructing a two-part heterogeneous network based on the disease similarity, the metabolite similarity and the known metabolite-disease relationship network, and predicting the disease-related metabolites in the heterogeneous network by utilizing a double random walk method. Tests prove that the method disclosed by the invention can predict the disease-related metabolites, provides a basis for biological experiments and improves diagnosis and treatment level of the disease.

The present invention relates to nucleic acid and amino acid sequences that confer herbicide resistance in plants, as well as herbicide resistance in plants, plant seeds, plant tissues and plant cells comprising such sequences. In a preferred embodiment, the sequences of the present invention confer a tolerant phenotype in plants in response to a chronic and / or acute inhibitin dose of auxinic herbicides. The present invention also provides homologous sequences with a high degree of functional similarity.

The invention relates to the field of data mining in bioinformatics, in particular to a synthetic lethal interaction prediction method based on a heterogeneous graph convolutional neural network. The method mainly comprises the following steps: (1) collecting known synthetic lethal correlation data, gene GO information data and gene PPI data; (2) carrying out gene GO similarity analysis, measuring GO function similarity between genes by utilizing a semantic gene function similarity measurement algorithm, and constructing GO function similarity-based features of the genes; (3) constructing genes based on PPI characteristics, constructing a correlation network between proteins by utilizing protein correlation data, and obtaining the characteristics of each gene based on the protein correlation network in a random walk mode; (4) constructing an adjacent matrix by using synthetic lethal correlation data, and fusing domain features of the gene based on GO function similarity features and PPI features; and (5) constructing a synthetic lethal pair prediction model based on the graph convolutional neural network, predicting potential synthetic lethal interaction, and obtaining a final result.