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Related Experiment Video

Updated: May 31, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

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Published on: May 17, 2019

Knowledge based cluster ensemble for cancer discovery from biomolecular data.

Zhiwen Yu1, Hau-San Wongb, Jane You

  • 1School of Computer Science and Engineering, South China University of Technology, Guangzhou, China. zhwyu@scut.edu.cn

IEEE Transactions on Nanobioscience
|July 12, 2011
PubMed
Summary

This study introduces Knowledge-based Cluster Ensemble (KCE) for improved cancer classification using biomolecular data. KCE enhances accuracy and stability by integrating prior knowledge into cluster ensemble methods, outperforming existing techniques.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Last Updated: May 31, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Published on: May 17, 2019

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Cancer Research

Background:

  • Microarray techniques offer novel avenues for cancer diagnosis and treatment.
  • Accurate cancer classification using biomolecular data is crucial for effective diagnosis and treatment.
  • Current single clustering algorithms for cancer classification lack robustness, stability, and accuracy.

Purpose of the Study:

  • To develop a robust and accurate cluster ensemble approach for cancer classification.
  • To incorporate prior knowledge into the cluster ensemble framework to improve performance.
  • To address the limitations of existing single clustering and conventional cluster ensemble methods.

Main Methods:

  • Proposed a novel Knowledge-based Cluster Ensemble (KCE) approach.
  • Represented prior knowledge as pairwise constraints.
  • Utilized spectral clustering (SC) to generate multiple clustering solutions.
  • Transformed constraints into confidence factors and constructed a consensus matrix for final partitioning.

Main Results:

  • KCE demonstrated robust, stable, and accurate performance on cancer datasets.
  • KCE outperformed state-of-the-art single clustering algorithms.
  • KCE surpassed conventional cluster ensemble approaches in cancer classification tasks.

Conclusions:

  • KCE is an effective method for cancer classification using biomolecular data.
  • Integrating prior knowledge significantly enhances the performance of cluster ensemble methods.
  • KCE offers a more reliable approach for cancer type discovery and classification.