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TiMEG: an integrative statistical method for partially missing multi-omics data

15 Dec 2021

Abstract excerpt

Multi-omics data integration is widely used to understand the genetic architecture of disease. In multi-omics association analysis, data collected on multiple omics for the same set of individuals are immensely important for biomarker identification. But when the sample size of such data is limited, the presence of partially missing individual-level observations poses a major challenge in data integration. More...

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PMID: 34911979
DOI: 10.1038/s41598-021-03034-z

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