MOADE: a multimodal autoencoder for dissociating bulk multi-omics data

In single cell biology, the complexity of tissues may hinder lineage cell mapping or tumor microenvironment decomposition, requiring digital dissociation of bulk tissues. Many deconvolution methods focus on transcriptomic assay, not easily applicable to other omics due to ambiguous cell markers and...

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Veröffentlicht in:	Genome Biology Jg. 26; H. 1; S. 325 - 28
Hauptverfasser:	Sun, Jiao, Malik, Ayesha A., Lin, Tong, Bratton, Ayla, Pan, Yue, Smith, Kyle, Onar-Thomas, Arzu, Robinson, Giles W., Zhang, Wei, Northcott, Paul A., Li, Qian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 30.09.2025 Springer Nature B.V BMC
Schlagworte:	Accuracy Animal Genetics and Genomics Autoencoder Bioinformatics Biomedical and Life Sciences Brain Cancer Computational Biology - methods Deep learning DNA methylation Evolutionary Biology Gene Expression Profiling Genomics - methods High-resolution purification Human Genetics Humans Life Sciences Methodology Microbial Genetics and Genomics Multimodal Multiomics Origin cell mapping Pediatrics Plant Genetics and Genomics Proteomics Single-Cell Analysis - methods Software Tissues Transcriptome Transcriptomics Tumor microenvironment Tumors Multimodal Tumor microenvironment Autoencoder High-resolution purification Origin cell mapping
ISSN:	1474-760X, 1474-7596, 1474-760X
Online-Zugang:	Volltext
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