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Data Wisdom in Computational Genomics Research

Author

Listed:
  • Haiyan Huang

    (University of California)

  • Bin Yu

    (University of California
    University of California)

Abstract

All fields of science are now inundated with massive amounts of data, which have the potential to answer fundamental questions. Genomics is one particular example, exploring questions like: How does the human genome work? What genome variants make us more prone to diseases? To find answers to these questions, it is crucial to develop statistical and machine learning methods that can scale up, particularly through efficient data storage and communication. Equally crucial, but less emphasized, is the possession of data wisdom—a rebranding of the best elements of applied statistics in a recent note at ODBMS.org ( http://www.odbms.org/2015/04/data-wisdom-for-data-science/ ). The note at ODBMS.org contains ten sets of questions a practitioner can ask to cultivate data wisdom. Although there has been much recent excitement about big data, having enough data relevant to the problem is the key to gaining meaningful answers in genomics. Data wisdom gives us the insight into how these data would look, how much information a dataset really contains, and how to extract it. In this paper, we expand on the ten sets of questions and illustrate where and how data wisdom can be integrated into computational genomics research.

Suggested Citation

  • Haiyan Huang & Bin Yu, 2017. "Data Wisdom in Computational Genomics Research," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 646-661, December.
    • Handle: RePEc:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-016-9173-9
    DOI: 10.1007/s12561-016-9173-9
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    References listed on IDEAS

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    1. Teng, Siew Leng & Huang, Haiyan, 2009. "A Statistical Framework to Infer Functional Gene Relationships From Biologically Interrelated Microarray Experiments," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 465-473.
    2. Sapna Kumari & Jeff Nie & Huann-Sheng Chen & Hao Ma & Ron Stewart & Xiang Li & Meng-Zhu Lu & William M Taylor & Hairong Wei, 2012. "Evaluation of Gene Association Methods for Coexpression Network Construction and Biological Knowledge Discovery," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-17, November.
    3. Stephen Oliver, 2000. "Guilt-by-association goes global," Nature, Nature, vol. 403(6770), pages 601-602, February.
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