Nimrod Rappoport

This study identifies and characterizes circulating endothelial cells (CECs) using single-cell data, revealing their diverse origins and plasticity, and links them to angiogenic programs in cancer.

The paper develops spatial aging clocks to analyze cell proximity effects in mouse brain aging and rejuvenation, revealing pro-aging and pro-rejuvenating influences of specific cell types.

The paper uses plasma proteomics from the UK Biobank to estimate organ age and links it to disease risk and longevity.

INTEND integrates gene expression and DNA methylation data across disjoint samples, outperforming existing methods in cancer datasets.

MONET is a multi-omic clustering method that identifies sample modules with subset-specific clustering structures, showing robustness to missing data and relevance in cancer analysis.

NEMO is a multi-omics clustering algorithm that works with partial datasets, outperforming existing methods on full and imputed data, and is applied to AML patient data.

This paper reviews multi-omic and multi-view clustering algorithms and benchmarks them on cancer data from TCGA across ten cancer types.