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Multi-omic and multi-view clustering algorithms: review and cancer benchmark
[article]
2018
bioRxiv
pre-print
High throughput experimental methods developed in recent years have been used to collect large biomedical omics datasets. Clustering of such datasets has proven invaluable for biological and medical research, and helped reveal structure in data from several domains. Such analysis is often based on investigation of a single omic. The decreasing cost and development of additional high throughput methods now enable measurement of multi-omic data. Clustering multi-omic data has the potential to
doi:10.1101/371120
fatcat:xvteh7ld7ffgbmmer76xkn4v4m