A data structure and function classification based method to evaluate clustering models for gene expression data

易东; 杨梦苏; 黄明辉; 李辉智; 王文昌

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A data structure and function classification based method to evaluate clustering models for gene expression data

Updated：2026-03-12

- A data structure and function classification based method to evaluate clustering models for gene expression data
- A data structure and function classification based method to evaluate clustering models for gene expression data
- 解放军医学杂志（英文版） 2002年第4期页码：312-317
- Affiliations：
  
  1. Department of Medical Statistics
  2. Third Military Medical University
  3. ,Chongqing,400031
  4. Applied Research Centre for Genomics Technology
  5. Department of Biology & Chemistry
  6. City University of Hong Kong
  7. 83 Tat Chee Avenue
  8. Kowloon
  9. Department of Electronic Technology
  10. Southwest University of Politics and Law Science
- Author bio：
- Funds：
- DOI：
  中图分类号： R311
- 纸质出版：2002
- Accepted：
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A data structure and function classification based method to evaluate clustering models for gene expression data[J]. 解放军医学杂志（英文版）, 2002,(4):312-317.

[1]易东,杨梦苏,黄明辉,李辉智,王文昌.A data structure and function classification based method to evaluate clustering models for gene expression data[J].Journal of Medical Colleges of PLA,2002(04):312-317.
A data structure and function classification based method to evaluate clustering models for gene expression data[J]. 解放军医学杂志（英文版）, 2002,(4):312-317. DOI：

[1]易东,杨梦苏,黄明辉,李辉智,王文昌.A data structure and function classification based method to evaluate clustering models for gene expression data[J].Journal of Medical Colleges of PLA,2002(04):312-317. DOI：

摘要

Abstract

<正> Objective: To establish a systematic framework for selecting the best clustering algorithm and provide an evaluation method for clustering analyses of gene expression data. Methods: Based on data structure （internal information） and function classification （external information）

the evaluation of gene expression data analyses were carried out by using 2 approaches. Firstly

to assess the predictive power of clustering algorithms

Entropy was introduced to measure the consistency between the clustering results from different algorithms and the known and validated functional classifications. Secondly

a modified method of figure of merit （adjust-FOM） was used as internal assessment method. In this method

one clustering algorithm was used to analyze all data but one experimental condition

the remaining condition was used to assess the predictive power of the resulting clusters. This method was applied on 3 gene expression data sets (2 from the Lyer’s Serum Data Sets

and 1 from the Ferea’s Saccharomyces

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