Cancer subtype precise discovery and evolution analysis method based on data stream clustering

Abstract

The invention provides a cancer subtype precise discovery and evolution analysis method based on data stream clustering. The method comprises the following steps of (a) initialization of gene expression data stream; (b) online real-time clustering of the gene expression data stream: putting each reachable data point into a corresponding grid cell; performing online grid maintenance; and when the specific time node is reached, deleting a sparse grid according to the grid density information; (c) offline precise clustering of the gene expression data stream: regarding the grid as a virtual data point with the density information; clustering the virtual data point by using a clustering method based on density-distance distribution; performing fast clustering division on other data points according to the density information of the determined clustering center points; and finally outputting a clustering result; and (d) class cluster evolution migration analysis. The invention provides the cancer subtype precise discovery and evolution analysis method based on data stream clustering with high precision.

Description

technical field [0001] The invention relates to a cancer subtype discovery and evolution analysis method based on data flow clustering. Background technique [0002] Identification of cancer subtypes plays an important role in revealing disease pathogenesis and facilitating personalized therapy. After decades of research, uncertainties remain in the clinical diagnosis of cancer and the identification of tumor-specific markers. Therefore, the study of efficient biological data mining methods has become an important direction and an urgent need for the development of bioinformatics. [0003] As an advanced data analysis and knowledge discovery technology, cluster analysis has been successfully applied in many fields. In the field of bioinformatics, this technology has also shown its great potential. Especially in gene expression data analysis, cluster analysis has been widely used and become one of the main technical means. Regardless of the clustering algorithm, it is fir...

AI Technical Summary

Problems solved by technology

The SAMBA algorithm transforms the bi-clustering problem into a search problem of the maximum weight word graph in a bipartite graph based on a statistical model. It creates a new idea for the study of clustering technology in gene expression data analysis. The limitation is that the number of each vertex is limited. "degree" is a constant, which hinders the production of clustering results containing more objects or higher dimensions

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