Aditya Sriram

eQTM (expression quantitative trait methylation) Atlas: a comprehensive resource of over 11 million DNA methylation-gene expression associations through across 11 tissues and 4 diseases

eQTM Atlas, an R Shiny resource, curates over 11 million DNA methylation-gene expression associations from eight cohorts and 11 tissues to annotate EWAS CpGs beyond proximity.

DeepDiff-SHAP: Interpretable deep learning for subgroup-specific causal inference using conditional SHAP

DeepDiff-SHAP identifies subgroup-specific causal relationships in biomedical data using deep learning and SHAP, revealing clinically meaningful changes in feature interactions across patient subgroups.

DeepEXPOKE: A Deep Learning Framework with Polygenic Risk Scores as Knockoffs for Deconvoluting Genetic and Non-Genetic Exposure Risks in Sepsis and Coronary Heart Disease

DeepEXPOKE uses deep learning with knockoff features to distinguish genetic and non-genetic exposure risks in sepsis and coronary heart disease, identifying metabolites and asthma as risk factors.

Assessment of autism zebrafish mutant models using a high-throughput larval phenotyping platform

The paper develops a high-throughput phenotyping protocol for zebrafish larvae to assess autism-related mutant models, identifying morphological and behavioral defects in syngap1b and slc7a5 mutants.