Big Data, Real-World Data, and Machine Learning

Jing Lu, Yangyang Hao, Jing Huang () and Su Yeon Kim
Additional contact information
Jing Lu: Walmart Labs
Yangyang Hao: Veracyte Inc.
Jing Huang: Veracyte Inc.
Su Yeon Kim: KAIST

A chapter in Statistical Methods in Biomarker and Early Clinical Development, 2019, pp 167-195 from Springer

Abstract: 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.

Date: 2019
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DOI: 10.1007/978-3-030-31503-0_9

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