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Fast and Scalable Feature Selection for Gene Expression Data Using Hilbert-Schmidt Independence Criterion.

Mehrdad J Gangeh, Hadi Zarkoob, Ali Ghodsi

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |February 10, 2017
    PubMed
    Summary
    This summary is machine-generated.

    A new multivariate algorithm efficiently selects informative genes from large gene expression datasets. This method, based on the Hilbert-Schmidt independence criterion (HSIC), improves feature selection accuracy and scalability for big data analytics.

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    Area of Science:

    • Computational Biology
    • Bioinformatics
    • Genomics

    Background:

    • Selecting informative genes from high-dimensional microarray data is a significant challenge.
    • Thousands of genes in such datasets necessitate efficient feature selection methods.

    Purpose of the Study:

    • To develop a fast and scalable multivariate algorithm for identifying informative genes from gene expression data.
    • To quantify the dependence between gene expression data and response variables for improved gene subset selection.

    Main Methods:

    • Developed a novel feature selection algorithm based on the Hilbert-Schmidt independence criterion (HSIC).
    • Algorithm design was partly motivated by singular value decomposition (SVD).

    Main Results:

    • The algorithm demonstrated computational speed and scalability for large datasets.
    • Evaluated performance using synthetic and real-world data, showing high accuracy and stability of selected genes.
    • Particularly effective for datasets with multiclass response variables, extracting genes with high predictive capability.

    Conclusions:

    • The proposed method is memory-efficient, enabling scalability to massive, distributed datasets typical in big data analytics.
    • Offers a robust solution for feature selection in computational biology, enhancing the analysis of complex biological data.