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Big Data, Real-World Data, and Machine Learning

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Statistical Methods in Biomarker and Early Clinical Development

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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.

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Author information

Authors and Affiliations

  1. Walmart Labs, Sunnyvale, CA, USA

    Jing Lu

  2. Veracyte Inc., South San Francisco, CA, USA

    Yangyang Hao & Jing Huang

  3. KAIST, Daejeon, Republic of Korea

    Su Yeon Kim

Authors
  1. Jing Lu
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  2. Yangyang Hao
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  3. Jing Huang
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  4. Su Yeon Kim
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Editors and Affiliations

  1. MyoKardia Inc., South San Francisco, CA, USA

    Liang Fang

  2. BioMarin Pharmaceutical Inc., San Rafael, CA, USA

    Cheng Su

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Lu, J., Hao, Y., Huang, J., Kim, S.Y. (2019). Big Data, Real-World Data, and Machine Learning. In: Fang, L., Su, C. (eds) Statistical Methods in Biomarker and Early Clinical Development. Springer, Cham. https://doi.org/10.1007/978-3-030-31503-0_9

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