42 results about "Dendrogram" patented technology

A document correlation diagram drawing device includes extracting means (20, 30) for extracting content data and time data of document elements (E) each including one or more documents, dendrogram drawing means (50) for drawing a dendrogram showing a correlation between documents on the basis of the content data of the document elements, clustering means (70) for cutting the dendrogram in accordance with a predetermined rule and extracting clusters, and intra-cluster arranging means (90) for determining an intra-cluster arrangement of the document elements belonging to each cluster on the basis of the time data of the document elements. Accordingly, a dendrogram adequately showing the chronological development in each field can be automatically drawn.

Source data is analyzed and visualized for a user. As the user adjusts graphical query devices, the displayed data is filtered and the visualization is updated accordingly. The invention is particularly useful where the data relates to chemical compounds in that a SAR table is preferably generated, presented in the visualization, and updated according to the query device settings. The displayed SAR table entries may also be clustered as a function of similarity with respect to a chosen property, such as molecular weight or some topological characteristic. A hierarchy of similarity may then be indicated in the SAR table using, for example, a dendrogram.

The present invention is directed to a data processing apparatus and a computer implemented method for modeling and analyzing relational data represented in a network that includes a plurality of nodes and a plurality of connections between the nodes. The method includes assigning at least one weight to a connection between two nodes in the network. A set of possible dendrograms is then generated for the network, and a likelihood of each dendrogram in the set is determined. The determination of the likelihood is based on at least the one weight of the connection. One of the dendrograms from the set is selected as an optimal dendrogram based on the determined likelihood. The selected dendrogram is then output via an output device. The dendrogram may be used to predict missing links or identify any possible false-positive (noisy) links within a relational dataset.

Described is a system for automated community discovery in networks with multiple relational types. The system receives a network as input. The network comprises neighbors, edges connecting the neighbors, and vertices, where edges between two vertices represent a relation. A set of pair-wise similarity comparisons is computed for all pairs of relations. Two relations are considered similar if vertices connected to the two relations share similar relations to the same set of neighbors. A relation dendrogram is created based on the set of pair-wise similarity comparisons. Then, a cut in the relation dendrogram is selected to compute a community solution, resulting in a plurality of relation dendrogram partitions. Each relation dendrogram partition represents a community. A community density criterion is computed based on a density of each community calculated with respect to edge types contained within each community. Finally, a community solution is generated that maximizes the community density criterion.

Disclosed herein are methods and systems for identifying targets for distinguishing cell types. In some embodiments, the method comprises: hierarchically clustering expression profiles of cells to generate a dendrogram with each leaf representing the expression profile of a different individual cell. The dendrogram can be pruned by eliminating invalid splits (and their children). The remaining leaves in the dendrogram can be merged, independent of their locations in the dendrogram, based on their distances to one another to generate clusters of expression profiles. The method can identify targets for distinguishing cell types based on targets that are expressed differentially in the clusters.

The invention relates to a method for early screening of lung cancer by using a vacuum ultraviolet photoionization mass spectrometer, and belongs to the field of efficient and rapid non-invasive cancer screening. According to the method, lung cancer characteristic marking VOCs gas in human exhaled gas can be detected and analyzed directly. The method comprises the following steps: firstly, collecting gas exhaled by a person to be detected with a disposable aluminum foil gas sampling bag; then, enabling the gas to enter a vacuum ultraviolet photoionization VOCs mass spectrometer; processing sampling signals with a GHz threshold discrimination ion signal processer; identifying 20 types of components of the lung cancer characteristic marking VOCs gas in a data image on a computer, so as to compare and calculate with a lung cancer early screening algorithm dendrogram, and carry out risk assessment and early screening to judge whether a person suffers from lung cancer. The method can be applied to the aspects of screening of high-risk population, early detection of lung cancer, prognosis monitoring and the like, and has great significance in reducing the lethality of lung cancer.

The invention relates to a molecular marking method for carrying out genetic relationship and clustering analysis on citrus fruit flies and belongs to the technical field of molecular marking. The molecular marking method for carrying out genetic relationship and clustering analysis on the citrus fruit flies comprises the following steps: carrying out PCR (polymerase chain reaction) amplification on DNA of citrus fruit flies by utilizing seven RAPD (random amplification polymorphism DNA) primers and 10 pairs of SRAP (sequence-related amplified polymorphism) primer combinations, carrying out agarose gel electrophoresis on amplification products, taking pictures, recording results, and carrying out statistics on an amplification zone of a detected material; and analyzing with genetic analysis software to obtain genetic distance between individuals, and drawing a dendrogram of citrus fruit flies in different geographical populations for distinguishing different class groups. The molecular marking method for carrying out genetic relationship and clustering analysis on the citrus fruit flies has the advantages of stable reaction, clear amplified fragments and high polymorphism, a molecular reaction system for population genetic diversity of the citrus fruit flies is constructed, genetic diversity among populations can be shown, clustering analysis can be rapidly and accurately carried out on the citrus fruit flies, different populations can be distinguished, and the molecular marking method for carrying out genetic relationship and clustering analysis on the citrus fruit flies has an important significance on prevention and control of the citrus fruit flies.

The invention relates to the technique of development and application of spirulina and aims at providing a method for detecting production trait goodness and badness of spirulina strains. According to the method, a Tris-HC1 extracting solution is utilized for extraction so as to obtain water-soluble protein of spirulina cells; protein samples are separated by using sodium dodecyl sulfate-polyacrylamide gel electrophoresis, subsequently the optical density value of a protein zone at 102kD of a protein electrophoresis diagram is detected, and a dendrogram of the spirulina plants is established according to the value; if the optical density value of a candidate strain at the protein zone at the 102kD is larger than 140 and the candidate strain is gathered together with a known stain with good production nature, the stain is good in production nature and is applicable to large-scale cultivation and production; and if the optical intensity value of the protein zone at 102kD is less than 40 and the candidate strain is gathered together with known strains with poor production nature, the stain is poor in production nature and is not applicable to large-scale cultivation and production. The method is simple, efficient, reliable and low in cost, and complex tests and analysis on nucleic acid sequencing and bioinformatics comparison do not need to be carried out, therefore, the method is applicable to large-scale and high flux separation.

The invention provides a method for presenting visual search results on a mobile terminal. The method comprises the following steps: receiving a search condition, searching a matched target application according to the search condition, and obtaining associated applications having an associated relationship with the target application; generating a visual dendrogram comprising a root node icon and child node icons connected with the root node icon, wherein the root node icon is mapped to the target application, and the child node icons are mapped to the associated applications; representing the visual dendrogram on the mobile terminal. Correspondingly, the invention further provides a system for presenting visual search results on the mobile terminal. Through the implementation of the method and the device provided by the invention, the intuitive and demonstrative properties of the search result of the mobile application are improved, and meanwhile, the operability of the search results and the user experiences are also improved.

A document correlation diagram drawing device includes extracting means (20, 30) for extracting content data and time data of document elements (E) each including one or more documents, dendrogram drawing means (50) for drawing a dendrogram showing a correlation between documents on the basis of the content data of the document elements, clustering means (70) for cutting the dendrogram in accordance with a predetermined rule and extracting clusters, and intra-cluster arranging means (90) for determining an intra-cluster arrangement of the document elements belonging to each cluster on the basis of the time data of the document elements. Accordingly, a dendrogram adequately showing the chronological development in each field can be automatically drawn.

The invention discloses a gastroesophageal reflux disease danger factor determining method based on machine learning and a system thereof. The method and the system settle a problem of low accuracy indetermining the gastroesophageal reflux disease danger factor according to statistics. The method comprises the steps of constructing a user information set which comprises the gastroesophageal reflux disease danger factor, performing quantification processing on the factor in the user information set, and obtaining a quantification data matrix; secondly, performing standardization on the quantification data matrix, performing dimension reducing processing on the standardized matrix according to a main component analysis algorithm; then clustering the data in the processed data set by means of a hierarchy clustering algorithm, and obtaining a hierarchy clustering dendrogram; then according to a clustering number which is determined by the hierarchy clustering dendrogram, performing clustering dividing on the data in the processed data set according to the clustering number, and obtaining a plurality of class clusters; and finally calculating a correlation index among elements in eachclass cluster, and determining the element with highest correlation index as the gastroesophageal reflux disease danger factor.

Described is a system for feature selection that extends supervised hierarchical clustering to neural activity signals. The system generates, using a hierarchical clustering process, a hierarchical dendrogram representing a set of neural activity data comprising individual neural data elements having neural activity patterns. The hierarchical dendrogram is searched for an optimal cluster parcellation using a stochastic supervised search process. An optimal cluster parcellation of the hierarchical dendrogram is determined that provides a classification of the set of neural activity data with respect to a supervised classifier, resulting in a reduced neural activity feature set. The set of neural activity data is classified using the reduced neural activity feature set, and the classified set of neural activity data is decoded.

The invention discloses a wind power plant abandoned wind power assessment method based on a hierarchical clustering method. The method comprises the following steps: step S10, performing standardization moment flattening on actually measured wind speed data of a wind power plant; step S20, subjecting a wind speed matrix after the standardization moment flattening to empirical orthogonal function decomposition; step S30, based on the empirical orthogonal function decomposition result, calculating the variance contribution rate and the cumulative variance contribution rate of each spatial type; step S40, performing clustering analysis on the spatial matrix obtained by decomposing the empirical orthogonal function using the hierarchical clustering method; step S50, drawing a clustering dendrogram based on the result of hierarchical clustering analysis; step S60, dividing wind turbine generators based on the clustering dendrogram, selecting a mark post draught fan and determining distribution coefficients; and step S70, using the mark post draught fan method to calculate the amount of the abandoned wind power. The method of the invention can reduced errors in a mark post draught fan method, and relatively accurately reflect abandoned wind conditions in the wind power plant.

A method of comparing dendrograms created by different references or algorithms displays individual dendrograms (201, 202) on a screen in an overlapped yet distinguishable manner. The overlapped portions of the dendrograms are displaced vertically or horizontally from one another and indicated by different colors for individual dendrograms. A specified branch and elements hanging therefrom can be highlighted when displayed, such that differences in the manner in which individual elements of the overlapped dendrograms are clustered can be easily recognized.