ReviewNature reviews. Cancer2008
The properties of high-dimensional data spaces: implications for exploring gene and protein expression data.
Review in Nature reviews. Cancer, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 243 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
243 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Identifying correlations driven by influential observations in large datasets.Briefings in bioinformatics · 2022Pooled it
- Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma.Alimentary pharmacology & therapeutics · 2021Pooled it
- The rise of agentic AI in drug discovery from static tools to autonomous virtual scientists.npj drug discovery · 2026Review
- TargetPrior: a miRNA-signature embedded evolutionary learning framework for prioritizing drug targets in acute myeloid leukemia.Bioinformatics (Oxford, England) · 2026Article
- Peripheral blood transcriptomic signature is associated with cognitive impairment and freezing of gait in Parkinson's disease: a data-driven approach.NPJ Parkinson's disease · 2026Article
- Monocyte-Amplified Transcriptional Signatures of Human Diseases.bioRxiv : the preprint server for biology · 2026Article
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Improved prognostic survival models for pediatric medulloblastoma using high dimensional gene expression data.BMC medical genomics · 2026Article
- Review
- Statistical knockoffs improve biomarker discovery from transcriptomic data.Briefings in bioinformatics · 2026Article
- Comparative review of artificial intelligence for transcriptomic biomarker discovery in coronavirus disease 2019 (COVID-19).Briefings in bioinformatics · 2026Review
- When complexity does not pay: benchmarking deep learning and ensemble methods for biomarker discovery.Briefings in bioinformatics · 2026Article
- Radiomics-based artificial intelligence (AI) models in colorectal cancer (CRC) diagnosis, metastasis detection, prognosis, and treatment response prediction.Abdominal radiology (New York) · 2026Review
- Unsupervised learning reveals novel disease-associated proteins in high-dimensional human proteomic data.Scientific reports · 2026Article
- Modeling and application of alzheimer's disease complex trait prediction based on multi-task learning.Scientific reports · 2026Article
- Accurate prediction of candidate lncRNAs associated with DNA damage response based on gene expression patterns from graph neural networks.Bioinformatics advances · 2026Article
- Anti-inflammatory mechanisms ofInternational journal of ophthalmology · 2026Article
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- Generative epigenetic landscapes map the topology and topography of cell fates.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The evolution of Alzheimer's target identification: Towards a fusion of artificial and cellular intelligence.The journal of prevention of Alzheimer's disease · 2025Article
183 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.