30 results about "Protein protein interaction network" patented technology

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 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.