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Related Experiment Video

Updated: Apr 4, 2026

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies
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Double Selection Based Semi-Supervised Clustering Ensemble for Tumor Clustering from Gene Expression Profiles.

Zhiwen Yu, Hongsheng Chen, Jane You

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |September 11, 2015
    PubMed
    Summary

    Incorporating expert knowledge improves tumor clustering for cancer gene expression data. Novel frameworks like MDS-SSCE enhance tumor discovery and outperform existing methods.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Cancer Genomics

    Background:

    • Tumor clustering is crucial for cancer discovery from gene expression profiles, aiding diagnosis and treatment.
    • Existing algorithms often overlook valuable expert knowledge, limiting tumor discovery performance.

    Purpose of the Study:

    • To develop advanced semi-supervised cluster ensemble frameworks that integrate expert knowledge for improved tumor clustering.
    • To enhance the accuracy and reliability of tumor discovery from bio-molecular data.

    Main Methods:

    • Proposed feature selection based semi-supervised cluster ensemble (FS-SSCE) framework incorporating expert knowledge as constraints.
    • Developed double selection based semi-supervised cluster ensemble (DS-SSCE) with feature selection and optimal clustering solution subset selection.
    • Introduced modified double selection based semi-supervised cluster ensemble (MDS-SSCE) with enhanced solution selection strategies.

    Main Results:

    • FS-SSCE, DS-SSCE, and MDS-SSCE frameworks are effective for tumor clustering using bio-molecular data.
    • MDS-SSCE demonstrated superior performance compared to several state-of-the-art tumor clustering approaches across multiple datasets.
    • Feature selection effectively mitigates the impact of noisy genes, while expert knowledge integration refines clustering.

    Conclusions:

    • Semi-supervised cluster ensemble methods integrating expert knowledge offer a powerful approach for tumor discovery.
    • The proposed MDS-SSCE framework represents a significant advancement in tumor clustering accuracy and efficiency.
    • These findings have implications for improved cancer diagnosis and personalized treatment strategies.