Meghana S. Pagadala

This paper introduces NEMo, a machine learning model that predicts neoantigen immunogenicity using in vivo immune editing data, showing improved stratification of progression-free survival across multiple clinical settings.

This paper shows the HOXB13 p.G84E variant increases prostate cancer risk by threefold in men, but does not lead to more aggressive cancer.

The paper uses population-scale whole genome sequencing to identify EBV DNA as a biomarker for respiratory, autoimmune, and cardiovascular diseases and identifies genetic determinants of EBV persistence.

VADEr, a Vision Transformer-inspired framework, improves polygenic risk prediction for prostate cancer by capturing genetic interactions and revealing heterogeneity through DARTH scores.

This study identifies prevalence and disease risks for male and female sex chromosome trisomies using registry data from 1.5 million participants, finding shared phenotypic associations across trisomy subtypes.

This paper uses machine learning to predict ICB response by integrating germline and somatic features, revealing differences in immune pathways and clinical outcomes between MHC-I and MHC-II neoantigen reliant tumors.

This study shows that epigenetic variants affecting DNA methylation are distributed in chromatin domains and are linked to cancer risk and prognosis.

The paper examines the prevalence, morbidity, and mortality of sex chromosome aneuploidy in men from the MVP cohort, finding a prevalence of 1 in 370 males with higher healthcare utilization and poorer HRQoL but similar life expectancy compared to controls.