Bi-order genetic calculation-based gene expression data bi-clustering algorithm

A gene expression and biclustering technology, applied in the field of data mining processing, can solve the problems of large data volume, high dimensionality, high redundancy, etc., and achieve the effect of wide search range, high quality, and overcoming local information.
CN104573004AInactive Publication Date: 2015-04-29SOUTH CHINA UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
SOUTH CHINA UNIV OF TECH
Publication Date
2015-04-29
Estimated Expiration
Not applicable Β· inactive patent

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Abstract

The invention discloses a bi-order genetic calculation-based gene expression data bi-clustering algorithm. A kth column is subtracted from each column in a matrix M to obtain a matrix M (k), and k is equal to 1, 2, ..., n; hierarchical clustering is performed on each column of M (k) to obtain a set of biclustering seeds; performing genetic calculation to obtain a corresponding bicluster. According to the algorithm, the defect that the conventional genetic calculation-based biclustering algorithm can only perform selection aiming at the bicluster can be solved, optimization is performed on rows and columns simultaneously, the search efficiency can be improved, and a more superior biclustering analysis effect is obtained.
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Description

technical field

[0001] The invention relates to the field of data mining and processing, in particular to a biclustering algorithm of gene expression data based on two-stage genetic calculation. Background technique

[0002] The emergence and development of DNA microarray technology allows people to simultaneously detect thousands of genes and measure the expression level of their transcribed mRNA. Through repeated experiments under multiple experimental conditions (such as different experimental environments, different time points, and different tissue samples), gene expression data from hundreds of experiments can be collected. The rows of the gene expression data matrix represent the expression of a gene under different environmental conditions or at different time points, and the columns represent the expression of all genes under different conditions or samples (such as tissue, experimental conditions, processing factors, etc.), and the data in the matrix represent The...

Claims

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