Divyanshi Srivastava

The paper introduces Multi-ISM, a scalable framework for saturation mutagenesis that reduces computational cost while maintaining accuracy, and applies it to 5,000 genes to map regulatory elements and rare variant effects.

Sniff improves causal variant prioritization in GWAS by integrating Borzoi-predicted variant effects with functional annotations, identifying more fine-mapped variants with higher posterior inclusion probabilities.

This paper improves DNA CNNs' ability to predict indel effects on gene regulation by addressing technical variance through shift augmentation and introduces an interpretation technique validated with experimental data.

This paper introduces Borzoi, a model that predicts RNA-seq coverage from DNA sequence, enabling variant effect scoring across multiple regulatory layers and outperforming existing methods on QTLs.

GenoPipe identifies the genotype of origin in genomic datasets by analyzing unique markers to validate and correct experimental errors.

This study improves cross-species prediction of transcription factor binding by addressing domain shifts caused by species-specific repeats using domain adaptive neural networks.

The paper develops Bichrom, a bimodal neural network that models sequence and preexisting chromatin data to predict TF-DNA binding and shows preexisting chromatin improves binding specificity prediction for some TFs.

The paper investigates how posterior HOX TFs differ in their ability to bind inaccessible chromatin, leading to distinct gene expression patterns during motor neuron differentiation.