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  3. Weighted Sparse Partial Least Squares With Joint Sample And Feature Selection For Integrating Multi-omics Data.
  1. Home
  3. Weighted Sparse Partial Least Squares With Joint Sample And Feature Selection For Integrating Multi-omics Data.

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Weighted Sparse Partial Least Squares With Joint Sample and Feature Selection for Integrating Multi-Omics Data.

Wenwen Min, Taosheng Xu, Chris Ding

    IEEE Transactions on Computational Biology and Bioinformatics
    |September 3, 2025

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a new method for Sparse Partial Least Squares (sPLS) to identify specific sample subsets and remove outliers in data fusion. The novel approach enhances sPLS for improved multi-view data analysis and outlier detection.

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    Area of Science:

    • Computational Biology
    • Machine Learning
    • Statistical Analysis

    Background:

    • Sparse Partial Least Squares (sPLS) is a dimensionality reduction technique for data fusion.
    • Standard sPLS cannot identify latent subsets of samples or remove outliers.

    Purpose of the Study:

    • To develop a novel method for joint sample and feature selection in sPLS.
    • To extend sPLS for identifying specific sample subsets and outlier removal.
    • To adapt the method for multi-view data fusion.

    Main Methods:

    • Proposed an $\ell _\infty /\ell _{0}$-norm constrained weighted sparse PLS ($\ell _\infty /\ell _{0}$-wsPLS) for sample and feature selection.
    • Proved the Kurdyka-Łojasiewicz property of the $\ell _\infty /\ell _{0}$-norm constraints for global convergence.
  • Developed two multi-view wsPLS models and efficient iterative algorithms for multi-view data fusion.

    • Main Results:

    • The proposed $\ell _\infty /\ell _{0}$-wsPLS method enables joint sample and feature selection.
    • Globally convergent algorithms were developed for the proposed models.
    • Numerical and biomedical data experiments demonstrated the efficiency of the multi-view wsPLS methods.

    Conclusions:

    • The novel $\ell _\infty /\ell _{0}$-wsPLS method effectively identifies sample subsets and outliers.
    • The extended multi-view wsPLS models are efficient for multi-view data fusion.
    • The developed algorithms ensure convergence and demonstrate practical applicability.