AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs

Background Multi-omic studies provide comprehensive insight into biological systems by evaluating cellular changes between normal and pathological conditions at multiple levels of measurement. Biological networks, which represent interactions or associations between biomolecules, have been highly ef...

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Veröffentlicht in:	BMC bioinformatics Jg. 26; H. 1; S. 39 - 29
Hauptverfasser:	Boyd, Samuel S., Slawson, Chad, Thompson, Jeffrey A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 05.02.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Active module identification Algorithms Applications software Bias Bioinformatics Biological effects Biological networks Biological research Biological systems Biology, Experimental Biomedical and Life Sciences Biomolecules Cancer Carcinoma, Renal Cell - genetics Carcinoma, Renal Cell - metabolism Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Data analysis Data integration Datasets Electronic data processing Genomic analysis Graphs Humans Identification methods Kidney cancer Kidney Neoplasms - genetics Kidney Neoplasms - metabolism Life Sciences Methods Microarrays Modules Multi-omic data integration Multiomics Multiplexing Network analysis Probability Programming languages Proteins Public software Random walk Renal cell carcinoma Software Task complexity United States Multi-omic data integration Active module identification Biological networks
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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