Leon Schwartz

ExPert infers causal perturbations from cell states by learning prototypes across 12 contexts, achieving high recall with low compute and validating findings in disease models.

Keeping SCORE transforms diffusion models into probabilistic classifiers for genomics, providing accurate predictions with uncertainty and interpretability across multiple tasks.

CIPHER uses gene co-fluctuations in unperturbed cells to predict transcriptome-wide responses to perturbations, outperforming existing methods and revealing key regulatory structures.

The paper introduces NeEDL, a network medicine-based method for detecting higher-order epistatic interactions in complex diseases, showing improved statistical significance and applicability across eight diseases.

The paper introduces circRNA-sponging, a pipeline to analyze circRNA expression and miRNA sponging, identifying two types of circRNAs based on binding site ratios.

TF-Prioritizer identifies condition-specific transcription factors using multi-modal genomic data and generates an interactive web report.