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A detection method for intronic snoRNA genes using extended-weight-updating SOM with appearance probability of bases

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Abstract

Small nucleolar RNAs (snoRNAs) are known that they will participate with RNA modification. However, detail functions of snoRNAs have not been clear still yet. In order to make clear functions of snoRNA, finding more snoRNAs and studying their works in cells are required. In this paper, we propose a method to detect snoRNA genes using extended-weight-updating self-organizing map (eSOM). An input vector to eSOM consists of a feature vector and a target vector. Different from a conventional SOM, a winner node for an input vector is decided by the feature vector only, and all the weights around the winner node are updated to be close to the input vector. We employ bases appearance probabilities and complementary base pair ratio for a feature vector. A target vector is a flag which is 1.0 or 0.0 for a positive or a negative sample, respectively. Experimental results showed our method achieved 91 and 93 % detection ratio for boxC/D and boxH/ACA type snoRNA genes, respectively.

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References

  1. Tran E, Brown J, Stuart ME (2004) Evolutionary origins of the RNA-guided nucleotide-modification complexes: from the primitive translation apparatus. Trends Biochem Sci 29(7):343–350

    Article  Google Scholar 

  2. Sakakibara Y, Popendorf K, Ogawa N, Asai K, Sato K (2007) Stem kernels for RNA sequence analyses. J Bioinform Comput Biol 5(5):1103–1122

    Article  Google Scholar 

  3. Fukushima T, Yamamori K, Yoshihara I, Nagahama K (2008) Feature extraction of protein expression levels based on classification of functional food with SOM. Proceeding of the 13th international symposium on Artificial Life and Robotics, Beppu, Oita, Japan, 2008, pp 849–852

  4. Kohonen T (1996) Self organizing maps. Springer, Tokyo

    Google Scholar 

  5. http://snoopy.med.miyazaki-u.ac.jp/

  6. Sato K, Hamada M, Asai K, Kiryu H, Mituyama T (2009) CENTROIDFOLD: a web server for RNA secondary structure prediction. Nucleic Acids Res 37:277–280 (Web server issue)

    Article  Google Scholar 

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Correspondence to Kunihito Yamamori.

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Yamamori, K., Matsuo, T., Iwakiri, J. et al. A detection method for intronic snoRNA genes using extended-weight-updating SOM with appearance probability of bases. Artif Life Robotics 17, 405–411 (2013). https://doi.org/10.1007/s10015-012-0072-y

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  • DOI: https://doi.org/10.1007/s10015-012-0072-y

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