Pramod Bharadwaj Chandrashekar

Inferring virtual cell environments using multi-agent reinforcement learning

CellTRIP uses multi-agent reinforcement learning to infer dynamic virtual cell environments from single-cell gene expression data, enabling simulation and prediction of cell interactions and changes over time and space.

DeepCORE: An interpretable multi-view deep neural network model to detect co-operative regulatory elements

DeepCORE identifies co-operative regulatory elements by modeling genetic, epigenetic, and transcriptional patterns across 25 cell lines, outperforming existing methods and providing interpretable insights.

DeepGAMI: Deep biologically guided auxiliary learning for multimodal integration and imputation to improve phenotype prediction

DeepGAMI improves phenotype prediction from multimodal data by integrating biological knowledge and imputing missing modalities, outperforming existing methods and providing interpretable insights.

TreeMap: A Structured Approach to Fine Mapping of eQTL Variants

TreeMap identifies causal eQTL variants by accounting for multisite effects and linkage, showing improved accuracy and enabling genome-wide analysis of long-range eQTL.

Contextual Classifications of Cancer Driver Genes

This study introduces a computational method to classify cancer driver genes as oncogenes or tumor suppressors based on mutation patterns across 33 cancer types, identifying novel genes and revealing context-specific functions.