SiRCle (Signature Regulatory Clustering) model integration reveals mechanisms of phenotype regulation in renal cancer

Background Clear cell renal cell carcinoma (ccRCC) tumours develop and progress via complex remodelling of the kidney epigenome, transcriptome, proteome and metabolome. Given the subsequent tumour and inter-patient heterogeneity, drug-based treatments report limited success, calling for multi-omics...

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Vydáno v:	Genome medicine Ročník 16; číslo 1; s. 144 - 26
Hlavní autoři:	Mora, Ariane, Schmidt, Christina, Balderson, Brad, Frezza, Christian, Bodén, Mikael
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 04.12.2024 BioMed Central Ltd Springer Nature B.V BMC
Témata:	AI applications in cancer discovery and care Analysis Anopheles Bioinformatics Biological analysis Biomedical and Life Sciences Biomedicine Cancer Cancer Research Carcinoma, Renal Cell - genetics Carcinoma, Renal Cell - metabolism Care and treatment Clear cell renal cell carcinoma Clear cell-type renal cell carcinoma Cohort Studies Consortia Datasets Disease DNA DNA methylation Electron transport Enzymes Gene expression Gene Expression Regulation, Neoplastic Genes Genes, Neoplasm Genomes Genotype & phenotype Glycolysis Human Genetics Humans Integration Kidney cancer Kidney Neoplasms - genetics Kidney Neoplasms - metabolism Kidney Neoplasms - pathology Machine learning Medicine/Public Health Metabolomics Methylation Mitochondrial DNA Multi-omics Mutation Neoplasm Staging Pancreatic cancer Patients Phenotype Phenotypes Physiological aspects Proteins Proteomes Proteomics Regulation RNA Squamous cell carcinoma Statistics as Topic Survival analysis Systems Biology Transcription Factors - metabolism Transcriptomes Tumors Variational autoencoder Integration Machine learning Variational autoencoder Regulation Clear cell renal cell carcinoma Metabolism Multi-omics PanCan
ISSN:	1756-994X, 1756-994X
On-line přístup:	Získat plný text
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