title: The properties of high-dimensional data spaces: implications for exploring gene and protein expression data.
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.
authors: Department of Oncology and Lombardi Comprehensive Cancer Center, Georgetown University School of Medicine, 3970 Reservoir Road NW, Washington, DC 20057, USA.
authors: Robert
authors: R
authors: Clarke
authors: Habtom W
authors: HW
authors: Ressom
authors: Antai
authors: A
authors: Wang
authors: Jianhua
authors: J
authors: Xuan
authors: Minetta C
authors: MC
authors: Liu
authors: Edmund A
authors: EA
authors: Gehan
authors: Yue
authors: Y
authors: Wang
mesh_terms: D005786:Gene Expression Regulation; D005796:Genes; D016678:Genome; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009369:Neoplasms / Q000145:classification / Q000235:genetics*; D011379:Prognosis; D011506:Proteins / Q000235:genetics*; D020543:Proteome; D014158:Transcription, Genetic
chemical_list: D011506:Proteins; D020543:Proteome