Shushan Toneyan

Assessing Codon Language Models for Context-Aware Codon Optimization in Nucleic Acid-Based Medicines

This study benchmarks three codon language models for optimizing nucleic acid-based medicines, finding that while no single model excels across all tasks, they offer advantages in capturing translational context beyond codon frequency.

Epiregulon: Inference of single-cell transcription factor activity to predict drug response and drivers of cell state

Epiregulon infers single-cell TF activity using ATAC-seq and RNA-seq to predict drug response and identify drivers of cell state.

Interpreting Cis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics

This paper introduces CREME, a toolkit to interpret cis-regulatory interactions from deep neural networks, revealing gene regulation rules and functional sequence elements.

EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations

EvoAug improves genomic DNN generalization and interpretability through evolution-inspired data augmentations while preserving functional integrity.

Analysis of single-cell CRISPR perturbations indicates that enhancers act multiplicatively and provides limited evidence for epistatic-like interactions

The paper analyzes single-cell CRISPR perturbations to show enhancers act multiplicatively with limited evidence for epistatic interactions.

Evaluating deep learning for predicting epigenomic profiles

This study evaluates deep learning models for predicting chromatin accessibility, showing that quantitative models outperform binary ones in generalization and interpretability.

Deconvolution of Expression for Nascent RNA Sequencing Data (DENR) Highlights Pre-RNA Isoform Diversity in Human Cells