30 results about "Protein protein interaction network" patented technology

The invention discloses a method for identifying protein functions based on a protein-protein interaction network and network topological structure features. Firstly, a node and side-weighted protein-protection interaction network is established, wherein the node represents protein while the edge represents the interaction; then the nodes and the sides in the network are weighted by protein first-grade structural description and protein-protein interaction trust scoring; protection functional annotation data is collected to establish a data set, and a new protein with overall and local information network topological structure features is provided based on a graph theory; and finally, the protein functions are predicated by choosing features through adopting a minimum-redundancy maximum-correlation method and by modeling through a support vector machine. The protein function predication method is greatly better than the prior art, and has robustness on sequence similarity and sampling; and meanwhile, information of three-dimensional structure and the like of protein is not required, so that the method is simple, rapid, accurate and efficient, and the method is expected to be applied in the research fields of proteomics and the like.

The invention discloses a method for identifying key proteins in a protein-protein interaction network. According to the method, an undirected graph G is constructed according to the protein-protein interaction data, and the edge clustering coefficient of the graph is calculated. Compared with the prior art, the method provided by the invention has the advantages of combining the gene expression profile data and the gene function annotation information data on the basis of considering the topological structure characteristics of the protein-protein interaction network, and integrating three groups of data to predict the key proteins, so that the influence caused by the data noise of a single data source on the prediction correctness can be effectively decreased, and the key proteins in the network can be predicted through the key protein characteristics embodied by three types of data, such as the edge clustering coefficient in the protein-protein interaction network, the Pearson's correlation coefficient of the gene expression value and the gene function similarity index. According to the method, the identification correctness of the key proteins in the protein-protein interaction network can be remarkably improved, and abundant key proteins can be predicted once, so that the problem that the biological experiment method is high in cost and time-consuming is solved.

The invention provides a method of identifying protein compounds by using a fruit fly optimization method. The method comprises the steps of converting a protein-protein interaction network into a undirected graph, performing pretreatment on the edges and nodes of the protein-protein interaction network, establishing a dynamic protein-protein interaction network, setting parameters, forming fruit fly positions, matching fruit flies with the protein-protein interaction network, determining initialization fruit fly positions, determining the fruit fly odor concentration, updating the fruit fly positions, generating a protein compound, and filtering the protein compound. The method gives full consideration to the dynamic nature of the protein network, the protein compound inner core-attachment structure and the locality and wholeness of the protein-protein interaction network and can identify protein compounds accurately. The results of simulation experiments show that the performance of the indexes such as the accuracy and the recall ratio are excellent. Compared with other clustering methods, the method, based on the characteristics of the protein network and the protein compounds, realizes the protein compound identification process and improves the protein compound identification accuracy.

The invention discloses a method for recognizing a protein network compound based on a semantic density, which is specifically implemented at the steps that all the protein properties in a network data set are searched in a gene ontology base GO as for a protein-protein interaction network data set without a weight; based on searching results, similarity of connected proteins in the network data set is calculated by a semantic similarity calculation method based on gene ontology; according to obtained similarity results, the given protein-protein interaction network data set is converted into an undirected network data set with a weight, wherein nodes represent the proteins, borders stand for interactions among the proteins, and the similarity among the proteins is the weight of the border; and the protein compound can be recognized from a protein-protein interaction network, wherein recognition accuracy is high and time complexity is low.

The present invention provides methods and compositions that can be applied to screening protein-protein interaction networks, screening drug candidates that modulate protein-protein interactions for on- and off-target effects, detecting extracellular targets for which no native S. cerevisiae receptor exists, and re-engineering yeast agglutination in order to answer biological questions about yeast speciation and ecological dynamics

The invention discloses a recognition method of protein complexes. The method comprises following steps: constructing a weighted protein-protein interaction network, wherein weight value represents similarity between gene representing modes corresponding to proteins with codes interacting each other; recognizing nucleuses of protein complexes based on the weighted protein-protein interaction network; recognizing accessory proteins for the nucleuses of protein complexes based on the number of nuclear proteins interacting with non-nuclear proteins and weight; and combining recognized nucleuses and recognized accessory proteins to recognize the protein complexes. The method does not take internal structures of protein complexes into consideration and utilizes the feature of code interaction with high protein gene expression. Therefore, the method is capable of recognizing protein complexes with overlapping structures and the recognized protein complexes reflect actual internal structures of protein complexes.

The invention discloses a method for identifying key protein through a fruit fly optimization algorithm. The method includes the steps that a protein-protein interaction network is converted into an undirected graph, a dynamic protein-protein interaction network is constructed, the edges and nodes of the dynamic protein-protein interaction network are preprocessed, the position of a fruit fly group is randomly initialized, the food random direction and distance are searched for with the sense of smell, the taste concentration judgment value of each fruit fly individual is calculated, the odorconcentration value of each fruit fly individual is calculated, the highest odor concentration value in the current group is worked out, the fruit flies fly to the food with the sense of sight, and key protein is generated. By means of the method, the key protein can be accurately identified; the simulation experiment result shows that the method has good performance indexes including sensitivity,specificity, positive predictive value, negative predictive value, accuracy rate and recall rate harmonic value, precise value and the like; compared with other methods for identifying key protein, the method adopting the fruit fly optimization algorithm for identifying the key protein has certain advantages.

The invention provides a method for identifying a protein complex based on BSO (Brain Storm Optimization); the method comprises the following steps: by utilizing strong global optimization searching capability of a BSO algorithm, regarding a protein-protein interaction network as a full network connected graph, combining gene ontology annotation function information of the protein with a topological structure of the protein-protein interaction network to define a distance among protein nodes, and carrying out preliminary clustering according to an improved k-means algorithm; then, according to four optimization searching principles of the BSO algorithm, generating a new fitness value, respectively carrying out module internal and module external optimization searching operations on a protein module which is formed preliminarily, iterating in a circulative manner and searching a most optimal global solution; and at last, carrying out post processing process. The method disclosed by the invention can keep the diversity of a group in the optimization searching process, thereby avoiding getting into local optimization; the global optimization module division is obtained, and the protein complex with remarkable biological enrichment is obtained.

The invention discloses a method for identifying key proteins with an AFSO (artificial fish school optimization) algorithm. The method comprises steps as follows: a protein-protein interaction networkis converted into an undirected graph, a purified protein-protein interaction network is constructed, RNA gene expression values corresponding to proteins, GO comment information and degrees of proteins in known compounds are obtained, edges and nodes of the purified protein-protein interaction network are treated, known key proteins are selected as initial artificial fishes, the artificial fishes execute foraging behavior, random behavior, following behavior and swarm behavior, and the key proteins are produced. According to the method, the key proteins can be identified accurately; a simulation experiment result indicates that performance of indexes such as sensitiveness, specificity, a positive predictive value, a negative predictive value and the like is better; compared with other methods for identifying the key proteins, the method has the advantages that optimizing characteristics of artificial fish schools are combined with topological characteristics of the protein-protein interaction network to realize the key protein identification process, and the accuracy rate of the key protein identification is increased.

A method for identifying a key module and a key node of a biomolecular complex network by fusing multiple methods on the basis of a topological structure of the biomolecular complex network which may be such as a protein-protein interaction network, a gene expression regulation network, a biological metabolism network, an epigenetic network, a phenotypic network or a signaling network, comprising the following main steps: according to the biomolecular network and a module division for the network, comprehensively and quantitatively identifying the key module and key node on the basis of the topological structure by various measuring methods from multi-angle.

The invention discloses a method for identifying key protein using an artificial bee colony optimization algorithm of a foraging mechanism. The method comprises: converting a protein-protein interaction network to an undirected graph, obtaining ribonucleic acid genetic expression values corresponding to protein, preprocessing edges and nodes of the protein-protein interaction network, establishing a dynamic protein-protein interaction network, selecting known key protein as a honey source, honey bees searching neighbourhood of the honey source, following bees searching neighbourhood of the honey bees, updating the honey sources, investigating bees searching new honey sources in a global manner, updating the honey sources, and generating the key protein. The method can accurately identify the key protein. Results of simulation experiments show that sensitiveness, specificity, positive predictive values, negative predictive values and other performance indexes are relatively excellent. Compared with other key protein identification method, the identification process of key protein realized by combining optimizing characteristics of artificial bee colony with characteristics of the protein-protein interaction network improves identification accuracy rate of the key protein.

The invention discloses a method for establishing a protein-protein interaction network by utilizing text data. The method is characterized by comprising the following steps: (1) establishing a protein set; (2) recording the probability values of interactions of every two proteins in the protein set; (3) establishing an initial network structure according to the probability values; (4) repeatedly selecting protein, giving feedback values of positive or negative reactions, iterating discontinuously on the initial network structure to obtain a final network structure. According to the method, the probability graph of a reaction network is established through reinforcement learning in the mode of repeatedly selecting and interacting on the basis of positive feedback, negative feedback and prohibited feedback; the probability graph is combined seamlessly with biological knowledge and biological data.

The invention discloses a method and a system for determining cancer network markers based on a probability model. The method comprises the following steps: utilizing a probability density function, converting gene expression data matrices of all normal samples and disease samples obtained into likelihood matrices, and constructing a normal sample distribution function according to the likelihoodmatrices of all normal samples; each element in the likelihood matrix of each disease sample is then brought into the normal sample distribution function, the set of significantly different genes foreach disease sample is determined, and the set of significantly different genes for each disease sample is mapped to proteins. Protein-protein interaction networks, and network markers for each disease sample are identified. The method or system provided by the invention can accurately and effectively acquire cancer network markers, and utilize the cancer network markers to carry out subtype classification of diseases so as to realize precise diagnosis and treatment of diseases.

The invention relates to a method for predicting a function module and function on the basis of the PPI network hierarchy structure. According to the technical scheme, the method comprises the steps that PPI network and biological information are input, a hierarchy tree T is built according to a protein-protein interaction network, the hierarchy tree T is coded according to likelihood value calculation of the protein-protein interaction network, a genetic algorithm of a maximum likelihood value hierarchy tree T is sought, and function module mining and function prediction are conducted. Accordingly, the defects that in the sparse PPI network low in density, the effect is poor, and randomness exists are overcome. According to the method, mining and function prediction are conducted on the function module according to the maximum likelihood value hierarchy tree T, and function module mining and function prediction are conducted simultaneously through network likelihood value calculation;on the basis of considering network topology, corresponding biological information is blended, an internal relationship among network nodes is reflected, many unnecessary density calculations are reduced, the prediction result is more accurate, and the reliability of the prediction result is improved.

A system and a method for determining an association of at least one biological feature with a medical condition, in particularly, but not exclusively, a system and a method of determining an association of at least one biological feature in form of a gene expression with cancer or a subtype of cancer that can include the generation of a simplified protein-protein interaction network based on processed biological data. The system and respective method is especially suitable for analysis of high dimensional and low sample size biological datasets such as in cancer research.

The invention relates to a research method of an action mechanism of Chinese angelica and six-yellow decoction for treating ulcerative colitis. The intersection target of the active component and the disease is obtained through database analysis of TCMSP, uniprot and the like. A protein-protein interaction (PPI) network is constructed based on String, and a result is visually analyzed by using Cytoscape software to obtain important modules and core targets in the PPI network. Gene GO and KEGG pathway enrichment analysis are performed on a target spot by adopting Metascape, to obtain a component-target spot-gene function-pathway-disease visual graph, and an action mechanism is analyzed. The method provides a new method for researching the drug-target interaction mechanism of the Chinese angelica and six-yellow decoction for treating ulcerative colitis; the problems that the number of interactions between multiple active ingredients and multi-target protein is too large and screening is difficult are solved, and the method has important application significance for guiding research and development of traditional Chinese medicine compounds.

The invention relates to a protein-protein interaction network based gene set identification method, and belongs to the technical field of genes. The identification method comprises the following steps: finding out genes/proteins in direct interaction with a 'set A' from a 'set B', and naming as a 'node set B'; counting the number of each gene/protein in the 'node set B' in direct interaction with the 'set A', and naming as a dimensionality 'i'; calling out the interactive genes/proteins from the 'set A' through the 'node set B [i]' with different minimal dimensionalities 'i', and naming as a 'set A [i]'; counting the aggregate z-score of the 'set A [i]'; adopting the 'set A [i]' with the maximal aggregate z-score as the obtained gene set. The identification method can identify the gene sets more relevant to the biological processes, and is helpful for relevant researchers to carry out correlational research work.

The invention discloses a start-stop nerve-soothing decoction-autism core action gene target spot and a screening method thereof.The screening method includes the steps that intersection target spots of active ingredients and diseases are obtained through database analysis of TCMSP, uniprot and the like, then a protein-protein interaction (PPI) network is constructed, and a result is visually analyzed through Cytoscape software to obtain important modules and core target spots in the PPI network; and carrying out gene GO and KEGG pathway enrichment analysis on the target spot by adopting Metascap, so as to obtain a visual graph of component-target spot-gene function-pathway-disease. The method solves the problems that the number of interactions between multiple active components and multi-target proteins is too large and screening is difficult, and has important application significance for guiding research and development of traditional Chinese medicine compounds.

Predicting the metastasis of cancer in a patient from one tissue to another is disclosed. A computer-implemented method for predicting metastasis may comprise receiving an indication of at least one disrupted gene of the cancer; searching for data representing a gene-to-gene or protein-to-protein interaction network to determine the position of a received gene, wherein the data representing a gene-to-gene or protein-to-protein interaction network includes data that expresses the gene or the protein as a node of the network and expresses functional or physical interactions among the genes or proteins as an edge of the network; traversing the data representing a gene-to-gene or protein-to-protein interaction network specific for a type of the cancer type from a position of the received genein the network to a position of at least one gene involved in metastasis for a tissue type, organ or body part; determining at least one shortest path in the network between the received gene and theat least one gene involved in metastasis for the tissue type, organ or body part; generating a prediction of metastasis to the tissue type based on the at least one determined path; and generating anoutput display indicating a likelihood of spread of cancer to the tissue type, organ or body part.