Rebecca Elyanow

The paper identifies immune exposure signatures by clustering T-cell receptors co-occurring across donors, linking them to specific pathogens and HLA alleles.

The paper identifies CeD-specific TCRs in peripheral blood that classify celiac disease independently of gluten intake, showing higher predictive power than traditional TCRs.

The paper maps T-cell receptor β sequences to specific HLA allotypes using population-level TCR repertoire data, enabling accurate HLA imputation and revealing insights into TCR-HLA interactions.

NetMix improves subnetwork identification by reducing statistical bias in altered subnetwork estimation, outperforming existing methods on simulated and real data.

STARCH infers copy number aberrations from spatial transcriptomics data by leveraging spatial proximity, outperforming non-spatial methods.

netNMF-sc improves scRNA-seq analysis by imputing dropout events and reducing dimensionality using gene-gene interactions, outperforming existing methods at high dropout rates.

The paper introduces NAIBR, a method for identifying structural variants from linked-read sequencing data, showing improved performance over existing methods.