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

• Published in: BMC genomics Vol. 20; no. S11; pp. 944 - 11
• Main Authors: Ma, Tianle, Zhang, Aidong
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20.12.2019; BioMed Central; BMC
• Subjects: Algorithms; Autoencoder; Bias; Biochemistry; Biological interaction networks; 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; Machine Learning; Methylation; 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; Proteins; Regularization; Representations; Ribonucleic acid; RNA; Systems Biology - methods; Training; Deep learning; Graph regularization; Autoencoder; Multi-omics data; Data integration; Biological interaction networks; Multi-view learning

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 different layers o...