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

A technology of data flow clustering and analysis method, applied in the field of cancer subtype discovery and evolution analysis, can solve problems such as hindering the production of clustering results, and achieve the effect of high precision
CN107301328AActive Publication Date: 2017-10-27ZHEJIANG UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
ZHEJIANG UNIV OF TECH
Publication Date
2017-10-27

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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.
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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...

Claims

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