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Integrate multi-omics data with biological interaction networks using Multi-view Factorization AutoEncoder (MAE)
2019
BMC Genomics
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 of molecular feature spaces and is crucial to elucidate molecular pathways underlying various diseases. Machine learning approaches to mining multi-omics data hold great promises in uncovering
doi:10.1186/s12864-019-6285-x
pmid:31856727
pmcid:PMC6923820
fatcat:lmx32nlkwngvzgmv4tawn2yzei