Clustering method and system based on biological data, clustering result evaluation method and system

Abstract

The invention provides a clustering method based on biological data, comprising the following steps: a data acquisition step, a data classification step, a distance calculation step and a clustering step. The present invention also provides a method for evaluating clustering results based on biological data, comprising the following steps: a step of obtaining clustering results, a step of calculating clustering evaluation scores, a step of converting clustering evaluation scores, and a step of integrating decision-making. The invention also provides a biological data-based clustering system and a biological data-based clustering result evaluation system. The clustering method and system based on biological data, the clustering result evaluation method and system of the present invention improve the biological application value of the clustering result and have strong practicability.

Description

technical field [0001] The invention relates to the technical field of cluster analysis, in particular to a biological data-based clustering method and system, and a clustering result evaluation method and system. Background technique [0002] With the emergence of a large number of biological data, how to quickly and reasonably analyze various biological data has become a hot and difficult issue in the field of biology. Taking gene analysis as an example, the current technology can simultaneously detect the expression values ​​of tens of thousands of genes at one time, and obtain sequence information and functional information of genes through various channels. Therefore, how to cluster and analyze numerous genes and dig out genes with biological significance is one of the important research directions in this field. The commonly used method is to use the clustering method in the field of machine learning, but the general clustering method only evaluates the clustering and...

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Problems solved by technology

The commonly used method is to use the clustering method in the field of machine learning, but the general clustering method only evaluates the clustering and clustering results from the calculation point of view, while ignoring the meaning of the biological data itself, resulting in inconsistent clustering results. It has practical application value, and the applicability of clustering results is low

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