Veerabhadran Baladandayuthapani

This paper introduces a Bayesian multi-resolution spatial graphical regression framework to infer spatially varying gene networks from hierarchical spatial transcriptomics data, showing improved network recovery and identifying key regulatory hubs in kidney cancer.

This study shows that HPV16 E6 splicing variants influence oropharyngeal cancer aggressiveness by altering E-cadherin localization and clinical outcomes.

The paper identifies a transcriptional transition state in ccRCC cells that drives sarcomatoid transformation and enhances immunotherapy response via macrophage and T-cell interactions.

This paper introduces a spatial Graphical Regression model to analyze spatial transcriptomics data and identifies novel gene interactions in cancer.

This paper introduces ConnReg, a regression framework for analyzing brain functional connectivity networks, and applies it to the Human Connectome Project to study how connectivity varies with covariates.

This paper introduces iPCR, a model that predicts patient drug responses by integrating genomic data from cell lines and tumors, showing improved performance over existing methods.

The paper introduces DIMPLE, an R package to quantify, visualize, and model cell-cell interactions from multiplex imaging data, and demonstrates its application to uncover associations between interactions and patient covariates.

This paper introduces GraphR, a Bayesian method for probabilistic graphical modeling under sample heterogeneity, showing improved performance in network estimation and computational efficiency.

The paper introduces a Bayesian probabilistic framework to infer treatment trees from PDX data, identifying synergistic treatment combinations and their biological mechanisms across cancers.

SpaceX identifies gene co-expression networks in spatial transcriptomics data, revealing spatially regulated genes in mouse hypothalamus and human breast cancer.

This paper introduces a Bayesian framework to improve drug response prediction in cancer by integrating pharmacogenomic data across patients and cell lines.

The paper develops TransPRECISE, a Bayesian network framework, to analyze pan-cancer patient and cell line interactomes, identifying pathway activities and drug sensitivities across multiple cancer types.

The paper develops a network-based method to predict drug sensitivity for cancer patients using cell line and omics data, identifying effective drug-patient matches.