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Feature extraction of protein expression levels based on classification of functional foods with SOM

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Abstract

We investigated the relations between the physiological activities and protein expression levels of functional foods using a self-organizing map (SOM). The input vectors to the SOM were the protein expression levels and the physiological activity. A competitive node has two kinds of weights: one is for protein expression levels, and the other is for physiological activity. A winner node is decided by the distance between the values of protein expression levels described in the input vector and the corresponding weights only. Then all weights, including those for physiological activity in each node, are updated. Therefore each node has an artificially generated value of physiological activity. Finally, the nodes can be categorized by the abovementioned physiological activity. A well-trained SOM gives us information about the relations between physiological activities and protein expression levels.

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Authors and Affiliations

  1. Graduate School of Engineering, University of Miyazaki, Miyazaki, Japan

    Tamon Fukushima

  2. Faculty of Engineering, University of Miyazaki, 1-1 Gakuen Kibanadai Nishi, Miyazaki, 889-2192, Japan

    Kunihito Yamamori & Ikuo Yoshihara

  3. Miyazaki Prefectural Industrial Foundation, Sadowara, Japan

    Kiyoko Nagahama

Authors
  1. Tamon Fukushima
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  2. Kunihito Yamamori
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  3. Ikuo Yoshihara
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  4. Kiyoko Nagahama
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Corresponding author

Correspondence to Kunihito Yamamori.

Additional information

This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008

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Fukushima, T., Yamamori, K., Yoshihara, I. et al. Feature extraction of protein expression levels based on classification of functional foods with SOM. Artif Life Robotics 13, 543–546 (2009). https://doi.org/10.1007/s10015-008-0597-2

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  • DOI: https://doi.org/10.1007/s10015-008-0597-2

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