short-paper

Spectral feature selection and its application in high dimensional gene expression studies

Authors:

Yin LiuAuthors Info & Claims

BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Pages 314 - 320

https://doi.org/10.1145/2649387.2649396

Published: 20 September 2014 Publication History

Abstract

Many variable selection techniques have been proposed for clustering analysis of gene expression data. Motivated by spectral learning, we propose a new filtering method that uses the correlation between features and the eigenspace of sample similarity matrix as the variable selection criteria. Spectral algorithm states that a sample similarity matrix with q strongly connected components tends to have q piecewise almost constant eigenvectors representing a specific partition of the sample space. Using distance correlation metric, our proposed method, spectral correlation (Scorrelation) measures features' correlation with the top q eigenvectors of sample similarity matrix and then infers their ability in differentiating the underlying clusters of samples. Our method has been applied to large-scale gene expression datasets. Compared to other filtering methods, our method is more effective and provides better clustering results in terms of clustering error rate and the reliability of the selected features. Our framework can be easily extended to other types of datasets for addressing clustering and classification problems.

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Cited By

Wang ZXu WLiu Y(2015)Systematic and Integrative Analysis of Gene Expression to Identify Feature Genes Underlying Human DiseasesTranscriptomics and Gene Regulation10.1007/978-94-017-7450-5_7(161-185)Online publication date: 17-Oct-2015
https://doi.org/10.1007/978-94-017-7450-5_7

Index Terms

Spectral feature selection and its application in high dimensional gene expression studies
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection

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cover image ACM Conferences

BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

September 2014

851 pages

ISBN:9781450328944

DOI:10.1145/2649387

General Chairs:
Pierre Baldi
University of California, Irvine
,
Wei Wang
University of California, Los Angeles

Copyright © 2014 ACM.

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Published: 20 September 2014

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National Institutes of Health
University of Texas Health Science Center at Houston

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BCB '14: ACM-BCB '14

September 20 - 23, 2014

California, Newport Beach

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Cited By

Wang ZXu WLiu Y(2015)Systematic and Integrative Analysis of Gene Expression to Identify Feature Genes Underlying Human DiseasesTranscriptomics and Gene Regulation10.1007/978-94-017-7450-5_7(161-185)Online publication date: 17-Oct-2015
https://doi.org/10.1007/978-94-017-7450-5_7

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