Integrate multi-omics data with biological interaction networks using Multi-view Factorization AutoEncoder (MAE)

Background Comprehensive molecular profiling of various cancers and other diseases has generated vast amounts of multi-omics data. Each type of -omics data corresponds to one feature space, such as gene expression, miRNA expression, DNA methylation, etc. Integrating multi-omics data can link differe...

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Veröffentlicht in:	BMC genomics Jg. 20; H. Suppl 11; S. 944 - 11
Hauptverfasser:	Ma, Tianle, Zhang, Aidong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 20.12.2019 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Animal Genetics and Genomics Autoencoder Bias Biochemistry Biological interaction networks Biomedical and Life Sciences Biomedical data Cancer Clustering Criminal investigation Data analysis Data integration Data Mining Databases, Genetic Deep learning Deoxyribonucleic acid DNA DNA methylation Domains Factorization Gene expression Genes Genomics Humans Knowledge Knowledge Bases Learning algorithms Learning strategies Life Sciences Machine Learning Methylation Microarrays Microbial Genetics and Genomics MicroRNA Mining industry miRNA Models, Biological Molecular interactions Multi-omics data Multi-view learning Natural language processing Neoplasms - genetics Neoplasms - mortality Neoplasms - pathology Networks Patients Plant Genetics and Genomics Proteins Proteomics Regularization Representations Ribonucleic acid RNA Systems Biology - methods Training Deep learning Graph regularization Multi-omics data Autoencoder Data integration Biological interaction networks Multi-view learning
ISSN:	1471-2164, 1471-2164
Online-Zugang:	Volltext
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