Abstract

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.

Author affiliations

1. Department of Oncology and Lombardi Comprehensive Cancer Center, Georgetown University School of Medicine, 3970 Reservoir Road NW, Washington, DC 20057, USA.
2. Department of Physiology and Biophysics, Georgetown University School of Medicine, 3970 Reservoir Road NW, Washington, DC 20057, USA.
3. Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University School of Medicine, 3970 Reservoir Road NW, Washington, DC 20057, USA.
4. Bradley Department of Electrical and Computer Engineering, School of Engineering and Sciences Virginia Polytechnic Institute and State University, 4300 Wilson Boulevard, Arlington, Virginia 22203, USA.

Correspondence to: Robert Clarke^1,2 Email: clarker@georgetown.edu

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