Vaha Akbary Moghaddam

METANet: A supervised ensemble learning framework for reconstructing direct and functional tissue-specific transcription factor networks

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

Graph in Graph (GiG): A novel graph AI framework for integrating and interpreting whole-person healthmedical and omics data

This paper introduces GiG, a graph AI framework that integrates and interprets whole-person medical and omics data to prioritize biomarkers and improve disease diagnosis.

Identification of novel protective loci for executive function using the trail making test part B in the Long Life Family Study

This study identifies novel protective genetic variants associated with TMT-B performance using a family-based GWLS approach in the Long Life Family Study.

FISHNET: A Network-based Tool for Analyzing Gene-level P-values to Identify Significant Genes Missed by Standard Methods

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.

Gene-Embedded Multi-Modal Networks for Population-Scale Multi-Omics Discovery

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.

Multi-omics Integration Identifies Genes Influencing Traits Associated with Cardiovascular Risks: The Long Life Family Study

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.