Journal
NATURE REVIEWS CANCER
Volume 8, Issue 1, Pages 37-49Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/nrc2294
Keywords
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Funding
- NATIONAL CANCER INSTITUTE [R33CA109872, R01CA096483, R03CA119313, U54CA100970, P30CA051008] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [R33EB000830] Funding Source: NIH RePORTER
- NCI NIH HHS [54-CA100970, R33-CA109872, R03 CA119313, P30 CA051008, R33 CA109872, R03-CA119313, R03 CA119313-01A2, R01-CA096483, U54 CA100970, R01 CA096483, 1P30-CA51008] Funding Source: Medline
- NIBIB NIH HHS [R33 EB000830, R33-EB000830] Funding Source: Medline
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High-throughput genomic and proteomic technologies are widely used in cancer research to build better predictive models of diagnosis, prognosis and therapy, to identify and characterize key signalling networks and to find new targets for drug development. These technologies present investigators with the task of extracting meaningful statistical and biological information from high-dimensional data spaces, wherein each sample is defined by hundreds or thousands of measurements, usually concurrently obtained. The properties of high dimensionality are often poorly understood or overlooked in data modelling and analysis. From the perspective of translational science, this Review discusses the properties of high-dimensional data spaces that arise in genomic and proteomic studies and the challenges they can pose for data analysis and interpretation.
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