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Genome-Wide Association Analysis for Oat Genetics Using Support Vector Machines

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Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1306))

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Abstract

An approach, namely genome-wide association study (GWAS), is usually deployed in genetics research to associate specific genetic variations with particular diseases. This method can be scanning the genomes from a vast of sources and looking for genetic markers that can be used to discriminate phenotypes. In recent years, advancements in computational resources have proposed and published to provide robust tools supporting studies on GWAS. This study aims to propose a method based on machine learning on Oat sequences to provide insights into the genetic architectures to discriminate phenotypes comparing to a statistical analysis on Genome-Wide Association Analysis provided in a R package.

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Author information

Authors and Affiliations

  1. College of Information and Communication Technology, Can Tho University, Can Tho, 900100, Vietnam

    Hiep Xuan Huynh, Quyen Ngoc Pham & Hai Thanh Nguyen

  2. Center of Software Engineering, Duy Tan University, Da Nang, 550000, Vietnam

    Toan Bao Tran

  3. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Toan Bao Tran

Authors
  1. Hiep Xuan Huynh
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  2. Toan Bao Tran
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  3. Quyen Ngoc Pham
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  4. Hai Thanh Nguyen
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Corresponding authors

Correspondence to Hiep Xuan Huynh or Hai Thanh Nguyen .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

  4. Sungkyunkwan University, Suwon, Korea (Republic of)

    Tai M. Chung

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Huynh, H.X., Tran, T.B., Pham, Q.N., Nguyen, H.T. (2020). Genome-Wide Association Analysis for Oat Genetics Using Support Vector Machines. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2020. Communications in Computer and Information Science, vol 1306. Springer, Singapore. https://doi.org/10.1007/978-981-33-4370-2_33

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  • DOI: https://doi.org/10.1007/978-981-33-4370-2_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-4369-6

  • Online ISBN: 978-981-33-4370-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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