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Perfect Population Classification on Hapmap Data with a Small Number of SNPs

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Neural Information Processing (ICONIP 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4985))

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Abstract

The single nucleotide polymorphisms (SNPs) are believed to determine human differences and, to some degree, provide biomedical researchers a possibility of predicting risks of some diseases and explaining patients’ different responses to drug regimens. With the availability of millions of SNPs in the Hapmap Project, although large amount of information about SNPs is available, the tremendous size also causes a major challenge for research on SNPs. Inspired from the recent research work on population classification by Park et al (2006), we attempt to find as few SNPs as possible from the original nearly 4 millions SNPs to classify the 3 populations in the Hapmap genotype data. In this paper, we propose to first use a modified t-test measure to rank SNPs, and then combine the ranking result with a classifier, e.g., the support vector machine, to find the optimal SNP subset. Compared with Park et al’s result, our proposed method is more efficient in ranking features and classifying the three populations, i.e., we obtained perfect classification using only 11 SNPs in comparison with 82 SNPs used by Park et al.

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Author information

Authors and Affiliations

  1. College of Information Engineering, Xiangtan University, Xiangtan, Hunan, China

    Nina Zhou

  2. Nanyang Technological University, Block S1, 50 Nanyang Avenue, Singapore, 639798

    Lipo Wang

Authors
  1. Nina Zhou
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  2. Lipo Wang
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Editor information

Masumi Ishikawa Kenji Doya Hiroyuki Miyamoto Takeshi Yamakawa

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© 2008 Springer-Verlag Berlin Heidelberg

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Zhou, N., Wang, L. (2008). Perfect Population Classification on Hapmap Data with a Small Number of SNPs. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_82

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  • DOI: https://doi.org/10.1007/978-3-540-69162-4_82

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69159-4

  • Online ISBN: 978-3-540-69162-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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