A Weighted Gene Co-Expression Network Analysis (WGCNA) of breast cancer prognostic genes (derived from transcriptome data from the TCGA Genomics Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/)), and cancer hallmark genes.
SEEK ID: https://fairdomhub.org/assays/1392
Modelling analysis
Projects: Consensus Hallmark Annotation
Investigation: Cancer Hallmark Consensus
Study: Prognostic and Hallmark Gene Networks
Assay position:
Biological problem addressed: Biological Network Analysis
Organisms: No organisms
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Created: 22nd Jan 2021 at 16:09
Last updated: 5th Mar 2021 at 17:57
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Projects: SysMO DB, HUMET Startup, FAIRDOM, Consensus Hallmark Annotation, NL-Bioimaging FAIR Metadata Templates, FAIR Functional Enrichment, Benefit for All FAIR Data
Institutions: University of Leiden, LIACS
https://orcid.org/0000-0002-1279-5133Expertise: Biochemistry, Bioinformatics, Data Management
Tools: Data Management, Transcriptomics, Databases, Workflows, Web services, Taverna, Ontologies, semantic web
I am an Assistant Professor at Leiden University in the Leiden Institute of Advanced Computer Science. I am a bioinformatician and my research interests are in data integration. I use scientific workflows and semantic web technologies to integrate and analyse data in systems biology and functional genomics.
Projects: Consensus Hallmark Annotation, The evolution of Gene Ontology
Web page: Not specified
Data and experimental methods to support the work in the following paper:
Establishing Consensus Annotation for the Hallmarks of Cancer, 2020, Yi Chen, F.J.Verbeek and K.Wolstencroft, in submission
Programme: Hallmarks of cancer
Public web page: Not specified
Organisms: Homo sapiens
The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development. However, methods for translating these high-level concepts into data-level associations between hallmarks and genes (for high throughput analysis), vary widely between studies. In this investigation we compare cancer hallmark mapping strategies from different studies, based on Gene Ontology and biological pathway annotation. By analysing the semantic ...
Submitter: Katy Wolstencroft
Studies: Comparing Cancer Hallmark Descriptions, Evolution of Gene Ontology Terms, Prognostic and Hallmark Gene Networks
Assays: Analysing Changes to GO Biological Process, Annotation Consensus and GO Consensus, Hub genes of modules and enriched GO terms, Jaccard Index Prognostic Hallmark Genes, WGCNA Prognostic Hallmark Genes
Snapshots: No snapshots
This study examines how different hallmark gene datasets intersect with prognostic cancer genes
Submitter: Katy Wolstencroft
Investigation: Cancer Hallmark Consensus
Assays: Hub genes of modules and enriched GO terms, Jaccard Index Prognostic Hallmark Genes, WGCNA Prognostic Hallmark Genes
Snapshots: No snapshots
1222 patients
Investigations: Cancer Hallmark Consensus
Studies: Prognostic and Hallmark Gene Networks
Assays: WGCNA Prognostic Hallmark Genes
Including 91 genes and 1222 Breast cancer patient This is an input data for WGCNA
Investigations: Cancer Hallmark Consensus
Studies: Prognostic and Hallmark Gene Networks
Assays: WGCNA Prognostic Hallmark Genes
Including 289 genes and 1222 Breast cancer patient This is an input data for WGCNA
Investigations: Cancer Hallmark Consensus
Studies: Prognostic and Hallmark Gene Networks
Assays: WGCNA Prognostic Hallmark Genes
Including 277 genes and 1222 Breast cancer patients. This is an input data for WGCNA.
Investigations: Cancer Hallmark Consensus
Studies: Prognostic and Hallmark Gene Networks
Assays: WGCNA Prognostic Hallmark Genes
Including 294 genes and 1222 Breast cancer Patient. This is an input data for WGCNA
Investigations: Cancer Hallmark Consensus
Studies: Prognostic and Hallmark Gene Networks
Assays: WGCNA Prognostic Hallmark Genes