Sandeep Acharya

METANet predicts direct, functional TF targets across human tissues by integrating motif, cis-regulatory, and expression features, outperforming existing methods.

This study identifies age and survival-related transcriptomic signatures in longevity-enriched cohorts using RNAseq data from blood samples.

MassID provides near-complete metabolite annotation with probabilistic identification and FDR control in metabolomics data.

FISHNET identifies significant genes missed by standard methods by analyzing gene-level P-values with network and function-based approaches, showing improved replication rates in cardiovascular traits.

GEM-Net constructs multi-modal gene networks using harmonized omics data from the Long Life Family Study, revealing a microbiome-immune axis linked to metabolic health.

The paper identifies pleiotropic genetic factors shared between kidney function and sRAGE using GWAS and TWAS in the LLFS cohort.

The paper integrates multi-omics data to identify genes influencing cardiovascular risk traits in the Long Life Family Study, finding 64 significant genes and replicating 29 in the Framingham Heart Study.