ArticleNucleic acids research2018
LinkedOmics: analyzing multi-omics data within and across 32 cancer types.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,489 papers.
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Who cites it
1,489 citing papers in PubMed.
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- Development of K-CORE: a web-based platform for integrated clinico-genomic analysis.Life science alliance · 2026Article
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- ABHD17C-Mediated S-Depalmitoylation of BCL6B Enhances CD24 Transcription to Resist Macrophage Phagocytosis in Pancreatic Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- EMMA-STRAT: a multi-omics based machine learning framework for stratification of endometrial carcinoma molecular subtypes and MSI status.BioData mining · 2026Article
- Pan-cancer characterization of the pyrimidine catabolic enzyme UPB1 identifies a tumor-suppressive prognostic biomarker in skin cutaneous melanoma.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
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- Bimodality in pan-cancer proteomics reveals new opportunities for biomarker discovery.Genome biology · 2026Article
- IFITM3 promotes an M2-like tumor-supportive microenvironment in acute myeloid leukemia.Medical oncology (Northwood, London, England) · 2026Article
1,429 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
The LinkedOmics database contains multi-omics data and clinical data for 32 cancer types and a total of 11 158 patients from The Cancer Genome Atlas (TCGA) project. It is also the first multi-omics database that integrates mass spectrometry (MS)-based global proteomics data generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) on selected TCGA tumor samples. In total, LinkedOmics has more than a billion data points. To allow comprehensive analysis of these data, we developed three analysis modules in the LinkedOmics web application. The LinkFinder module allows flexible exploration of associations between a molecular or clinical attribute of interest and all other attributes, providing the opportunity to analyze and visualize associations between billions of attribute pairs for each cancer cohort. The LinkCompare module enables easy comparison of the associations identified by LinkFinder, which is particularly useful in multi-omics and pan-cancer analyses. The LinkInterpreter module transforms identified associations into biological understanding through pathway and network analysis. Using five case studies, we demonstrate that LinkedOmics provides a unique platform for biologists and clinicians to access, analyze and compare cancer multi-omics data within and across tumor types. LinkedOmics is freely available at http://www.linkedomics.org.
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