Manu Saraswat

Single-cell Deep Omics Regulatory Inference

A mechanistically constrained autoencoder that reconstructs enhancer-driven gene regulatory networks from single-cell ATAC–RNA data. Applied to >1M glioblastoma cells, scDoRI identified repressive transcription factor circuits regulating tumor plasticity—validated in vivo by slowing tumor growth and increasing survival in mouse models. This computation → wet-lab validation → therapeutic hypothesis pipeline demonstrates AI that doesn't just predict, but explains and enables intervention.

Personalized Gene Expression Prediction

A modular deep learning system predicting cell-type-specific gene expression from personalized genomes, combining genomic sequence with chromatin accessibility. Unlike Enformer/Borzoi (which predict from reference genomes), DeepGenoXcan is designed for individual genetic backgrounds—critical for precision medicine. Outperforms both linear TWAS models and deep neural network models on donor-level generalization.

Explainable Neural Networks for Genomics

One of the first fully transparent deep learning architectures for genomics. Demonstrates that high predictive performance and complete interpretability can coexist, producing motif-level explanations of transcription factor binding and chromatin accessibility predictions.

Domain-Informed Training Strategies

Systematic evaluation of how biological priors affect deep learning training. Tested TF family relationships, DNA-binding domain similarity, and cofactor information as pre-training signals. Established benchmarks showing biologically informed training outperforms naive approaches, especially in low-data regimes.