Tarak Nandi

The paper analyzes gut microbiome metadata from the NCBI SRA to assess its structure, completeness, and reusability for large-scale studies and AI model development.

The paper introduces ARCHER-LD, a GPU-accelerated method to compute long-range linkage disequilibrium at biobank scale, achieving significant speedups over existing tools.

This paper develops an open-source LLM-based pipeline to identify patients with rare genetic aortopathies using clinical notes, achieving high accuracy in recommending genetic testing.

The paper introduces a closed-loop framework for single-cell foundation models that incorporates perturbation data to improve prediction accuracy, identifying therapeutic targets and pathways in RUNX1-familial platelet disorder.

The paper identifies pleiotropic loci across 1,167 traits using GWAS meta-analysis and shows they are enriched for functional annotations and improve polygenic score predictions.

The paper develops a multi-task learning approach to create context-specific gene network representations, applying it to colorectal tumors to predict therapeutic targets.

Population-stratified GWAS of 2,068 traits in 635,969 Million Veteran Program participants, 29% genetically non-European, yields 98,485 associations and 57,601 fine-mapped signals with largely shared cross-population architecture.

This paper optimizes the SAIGE algorithm using GPUs to accelerate GWAS on large datasets, achieving 20x speedup on the Million Veteran Program data.

The paper identifies monocyte-endothelial interaction alterations in TET2 CHIP and shows that targeting CXCR2 and ICAM1 can restore normal interactions.

This paper introduces RNA-Cascaded-Diffusion-Model to generate synthetic whole-slide image tiles from RNA-Seq data, improving machine learning model performance in low-data settings.