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Feature Selection for Genomic Signal Processing: Unsupervised, Supervised, and Self-Supervised Scenarios

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Abstract

An effective data mining system lies in the representation of pattern vectors. For many bioinformatic applications, data are represented as vectors of extremely high dimension. This motivates the research on feature selection. In the literature, there are plenty of reports on feature selection methods. In terms of training data types, they are divided into the unsupervised and supervised categories. In terms of selection methods, they fall into filter and wrapper categories. This paper will provide a brief overview on the state-of-the-arts feature selection methods on all these categories. Sample applications of these methods for genomic signal processing will be highlighted. This paper also describes a notion of self-supervision. A special method called vector index adaptive SVM (VIA-SVM) is described for selecting features under the self-supervision scenario. Furthermore, the paper makes use of a more powerful symmetric doubly supervised formulation, for which VIA-SVM is particularly useful. Based on several subcellular localization experiments, and microarray time course experiments, the VIA-SVM algorithm when combined with some filter-type metrics appears to deliver a substantial dimension reduction (one-order of magnitude) with only little degradation on accuracy.

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Notes

  1. For such a huge dimensionality, a preliminary Signal-to-Noise ratio (SNR)-based filtering method can be applied to weed out those k-mers patterns (i.e. columns) that are below certain low threshold.

  2. In addition to the SNR-type filter and SVM-RFE, there exist an extremely large number of application studies based on microarray data. Two recent ones are the MRMR [38] and Markov blanket [39], which are based on the Multivariate techniques. Another recent approach is the VIA-SVM [40], which is more amendable to the self-supervised scenario explained in Section 6.

  3. Downloadable from the official site http://www.genome.wi.mit.edu/mpr

  4. Here, we use bold face to represent both vectorial data such as gene expression profiles and non-vectorial data such as sequences.

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Acknowledgements

This work was in part supported by The Research Grant Council of the Hong Kong SAR (Project No. PolyU 5241/07E, PolyU 5251/08E, and A-PH18).

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Authors and Affiliations

  1. Princeton University, Princeton, NJ, USA

    S. Y. Kung & Yuhui Luo

  2. National Chung Hsing University, 250 Kuo Kuang Rd., Taichung, 402, Taiwan, Republic of China

    S. Y. Kung

  3. Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR

    Man-Wai Mak

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  1. S. Y. Kung
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Correspondence to Yuhui Luo.

Additional information

Based on SY Kung’s Keynote Paper, Proceedings, IEEE Workshop on Machine Learning for Signal Processing, Thessaloniki, Greece, August 27–29, 2007.

The research was conducted in part while S.Y. Kung was on leave with the National Chung-Hsing University as a Chair Professor.

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Kung, S.Y., Luo, Y. & Mak, MW. Feature Selection for Genomic Signal Processing: Unsupervised, Supervised, and Self-Supervised Scenarios. J Sign Process Syst 61, 3–20 (2010). https://doi.org/10.1007/s11265-008-0273-8

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