IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-31503-0_9.html
   My bibliography  Save this book chapter

Big Data, Real-World Data, and Machine Learning

In: Statistical Methods in Biomarker and Early Clinical Development

Author

Listed:
  • Jing Lu

    (Walmart Labs)

  • Yangyang Hao

    (Veracyte Inc.)

  • Jing Huang

    (Veracyte Inc.)

  • Su Yeon Kim

    (KAIST)

Abstract

Complex human diseases result from the cumulative effect of multiple genomic components and environmental factors. The impact of any individual marker is limited when diagnosing complex polygenic human disease or guiding efficient treatment. Large-scale studies of gene expression have much more chance to capture the signal from human disease. Meanwhile, sequencing technology is rapidly advancing enabling us to evaluate millions of genomic features simultaneously. Combined with clinical, demographic, proteomic, and imaging data, each patient provides an unprecedented amount of information on a meta-omics level. Machine learning becomes key to efficiently mining this big data and providing each patient the most effective personalized care.

Suggested Citation

  • Jing Lu & Yangyang Hao & Jing Huang & Su Yeon Kim, 2019. "Big Data, Real-World Data, and Machine Learning," Springer Books, in: Liang Fang & Cheng Su (ed.), Statistical Methods in Biomarker and Early Clinical Development, pages 167-195, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-31503-0_9
    DOI: 10.1007/978-3-030-31503-0_9
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-030-31503-0_9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.