Manu Shivakumar

The paper quantifies the divergence between polygenic and proteomic risk scores in neurodegenerative diseases by analyzing absorption and co-expression modules across multiple cohorts.

The paper evaluates proteomic and polygenic risk scores for disease prediction, finding proteomic scores outperform polygenic scores in most traits and show temporal responsiveness.

This study evaluates the reliability of coronary artery disease polygenic risk scores across diverse populations, finding poor individual agreement despite equivalent population-level performance.

This study uses multivariable Mendelian randomization to show that higher bone mineral density causally increases osteoarthritis risk independently of body mass index.

The paper performs a genome-wide rare variant analysis across nine cancer types using germline WES data to identify cancer predisposition genes and pathways.

The paper introduces iDRW, a multi-layered network-based pathway activity inference method, showing improved outcome prediction in urologic cancers compared to existing methods.

NETMAGE generates interactive phenotype networks from PheWAS data to visualize shared genetic associations and identify pleiotropy, aiding in drug target discovery.

This paper conducts a PheWAS using a Korean health check-up database and compares results across ethnic groups to identify phenotype-genotype associations and their clinical relevance.

This study develops a multi-view feature selection method to predict ovarian cancer survival using multi-omics data and shows it outperforms single-view and baseline fusion approaches.

The paper improves imputation of missing lab measures in IBD EHR data using latent-based analysis and clustering of comorbidities.