Dorota Matelska

This study shows that genetic diversity improves the performance of intolerance metrics in identifying disease-related genes, with multi-ancestry data outperforming European-only datasets.

The paper identifies plasma proteomic markers linked to polygenic risk of type 2 diabetes and related comorbidities using proteogenomic data from biobanks and clinical trials.

The paper uses machine learning to identify undiagnosed cases in UK Biobank data, improving gene-disease associations and discovering novel relationships.

The paper identifies rare protein-coding variants influencing plasma protein levels in 50,829 UK Biobank participants using exome-wide association studies.

The paper introduces OncMTR, a metric identifying genic regions intolerant to germline variation but enriched for cancer-driving mutations, and shows it improves genetic signal detection for hematologic malignancies.

The paper identifies a distinct early elongation phase in archaea where TEC escape regulates transcription output and responds to oxidative stress.

This study characterizes the temporal transcriptome and promoter architecture of African Swine Fever Virus during early and late infection, revealing distinct promoter motifs and transcription termination mechanisms.