32 results about "Group lasso" patented technology

The invention discloses a method for clustering lasso cluster characteristics based on network analysis, which is mainly used for solving the clustering of related expressed genes in bioengineering and establishing a lasso cluster model by using the method. The method comprises the steps of firstly, dividing experimental data into a positive data set and a negative data set, and respectively establishing a weighting co-expression gene network (adjacent matrix) corresponding to the positive data set or the negative data set; secondly, determining important network modules respectively corresponding to a positive sample and a negative sample; thirdly, simplifying a positive sample network module by utilizing a negative sample network module according to the experiment background; finally, clustering the characteristics according to the simplified positive sample network module, and establishing a lasso cluster and a promotion model of the lasso cluster according to the clustering. According to the method disclosed by the invention, the characteristic clustering of the lasso cluster is successfully and organically combined with module selection of network analysis, the operation is easy, the clustering method can be applied to screening of related genes in liver regenerative cell proliferation, and the method has an important application value in simulating a cell proliferation process of complicated diseases such as liver cirrhosis and cancer and screening new drugs.

A small-sample-based genotype-phenotype association analysis method in multi-omics data is disclosed, which specifically includes the following steps: generating a weighted undirected gene association graph using protein networks and gene expression values, and using the SPICi clustering method to The undirected graph is clustered to generate gene clusters; the gene clusters are screened by the group Lasso method; the SNP clusters corresponding to the screened gene clusters are obtained through the eQTL data; each SNP cluster, the corresponding gene cluster and the phenotype It is constructed as a three-layer network class block, and the sparse partial least squares method is used for regression operation on the relationship between SNP and gene in each class block, and logistic regression is used for operation on the relationship between gene and phenotype; The results are averaged to obtain the final prediction result. The invention can solve the problem that the eigenvalue is huge and cannot be effectively returned under the condition of small samples in the three-layer network; the prediction accuracy rate is improved; the biological meaning is clearer; and the tissue specificity is considered.

The invention discloses an evaluation method of excess sludge electroosmosis deep dehydration performance, belonging to the technical fields of sewage treatment and solid waste treatment. The method comprises the steps of firstly, collecting a biological sludge sample, and measuring the initial moisture content (IMC), volatile solids (VSS), electrical conductivity (EC), pH value, zeta potential and extracellular polymeric substance (EPS) content of the sludge sample before electroosmosis dehydration is carried out; screening out the key indexes, i.e., EC, Z and EPS, which affect the electroosmosis deep dehydration performance, by using a Group Lasso model; establishing a regression equation based on the key indexes, and calculating according to the regression equation so as to obtain sludge moisture content (FMC) and dehydration rate (DR) after the electroosmosis dehydration is finished. The evaluation method can quantify a complex relationship between residual sludge properties and electroosmosis deep dehydration performance so as to obtain a better prediction result; the result is high in conformity with measured data of the FMC and the DR which are obtained after the electroosmosis dehydration of a simulation device is finished, so that the test work load is greatly reduced, and basis data is provided for further sludge modification.

A method for constructing a dictionary to represent data from a training data set comprising: modeling the data as a linear combination of columns; modeling outliers in the data set via deterministic outlier vectors; formatting the training data set in matrix form for processing; defining an underlying structure in the data set; quantifying a similarity across the data; building a Laplacian matrix; using group-Lasso regularizers to succinctly represent the data; choosing scalar parameters for controlling the number of dictionary columns used to represent the data and the number of elements of the training data set identified as outliers; using BCD and PG methods on the vector-matrix-formatted data set to estimate a dictionary, corresponding expansion coefficients, and the outlier vectors; and using a length of the outlier vectors to identify outliers in the data.