Arvind U.K. Rao

This paper introduces a quantum Hamiltonian-based model for gene regulatory network inference, showing improved performance on synthetic and real single-cell RNA-seq data.

A bifunctional AR–p300/CBP molecule, DALTAC-1, forces a non-native contact between AR and p300 domains, silences AR enhancers and regresses AR-driven prostate tumors in mice while sparing AR-negative lines.

The paper analyzes matched pre- and post-chemotherapy biospecimens in pancreatic cancer, revealing that cancer-associated fibroblasts undergo neural reprogramming, characterized by axonal guidance gene enrichment, which may contribute to chemoresistance and tumor progression.

This study identifies lipid-laden macrophages as an immunosuppressive niche in PCNSL, validated through spatial transcriptomics and mouse models, and shows SYK inhibition reverses their tumor-supportive activity.

The paper identifies dysregulated pathways and candidate driver genes in skeletal muscle from individuals with type 2 diabetes using co-expression networks and single-nucleus RNA-seq data.

This paper introduces GP-GHS, a Bayesian method for inferring spatially varying cell-cell interaction networks from multiplexed tissue imaging data, identifying differentially active edges in colorectal cancer.

This study uses spatial transcriptomics to analyze glioblastoma tumor heterogeneity, identifying spatially distinct transcriptional patterns and signaling pathways that inform therapeutic strategies.

This paper applies a multitype Gibbs point process model to colorectal cancer imaging data to analyze spatial interactions between cell types in the tumor microenvironment.

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.