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Data Intensive Computing: A Biomedical Case Study in Gene Selection and Filtering

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Handbook of Data Intensive Computing

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Abstract

In data intensive computing environments where the number of samples and data dimensions grow sufficiently large, existing methods in Bioinformatics research are not effective for selecting important genes. In this chapter, we propose two approaches for parallel selection of genes, both are based on the well known { ReliefF} feature selection method and cluster computing environments. In the first design, denoted by { PReliefF} p , the input data are split into non-overlapping subsets assigned to cluster nodes. Each node carries out gene selection by using the { ReliefF} method on its own subset, without interaction with other clusters. The final ranking of the genes for selection is generated by gathering weight vectors from all nodes. In the second design, namely { PReliefF} g , each node dynamically updates global weight vectors so the gene selection results in one node can be used to boost the selection process for other nodes. Experimental results from real-world microarray expression data show that { PReliefF} p and { PReliefF} g nearly perfectly speedup to the number of nodes involved in the computing. When combined with several popular classification methods, the classifiers built from the genes selected from both methods have the same or even better methods than the genes selected from the original ReliefF method.

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Notes

  1. 1.

    The project described was supported by Award Number R01GM086707 from the National Institute Of General Medical Sciences (NIGMS) at the National Institutes of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or NIH.

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Author information

Authors and Affiliations

  1. Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA

    Michael Slavik, Imad Mahgoub & Taghi Khoshgoftaar

  2. Centre for Quantum Computation and Intelligent Systems, University of Technology, Sydney, NSW, 2007, Australia

    Xingquan Zhu

  3. Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL, 33431, USA

    Ramaswamy Narayanan

Authors
  1. Michael Slavik
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  2. Xingquan Zhu
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  3. Imad Mahgoub
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  4. Taghi Khoshgoftaar
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  5. Ramaswamy Narayanan
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Corresponding author

Correspondence to Xingquan Zhu .

Editor information

Editors and Affiliations

  1. Dept. of Computer Science & Engineering, Florida Atlantic University, Boca Raton, 33431, Florida, USA

    Borko Furht

  2. LexisNexis, Boca Raton, 33487, Florida, USA

    Armando Escalante

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Slavik, M., Zhu, X., Mahgoub, I., Khoshgoftaar, T., Narayanan, R. (2011). Data Intensive Computing: A Biomedical Case Study in Gene Selection and Filtering. In: Furht, B., Escalante, A. (eds) Handbook of Data Intensive Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1415-5_22

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  • DOI: https://doi.org/10.1007/978-1-4614-1415-5_22

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