Donna K. Slonim

flashscenic accelerates single-cell GRN analysis using GPU acceleration, improving speed and enabling benchmarking of integration strategies in AML data.

This study evaluates fasudil's effects on trisomy 21 neural progenitor cells, showing increased proliferation and reduced inflammation.

This paper introduces Dropout Augmentation and DAZZLE, a GRN inference method that improves performance by addressing dropout in single-cell data through model regularization.

This paper introduces RegDiffusion, a diffusion model for gene regulatory network inference, showing superior performance on single-cell datasets.

The paper shows that Hofbauer cells and fetal brain microglia share transcriptional profiles and responses to maternal diet-induced obesity, suggesting Hofbauer cells as a noninvasive biomarker for fetal microglial programming.

MELISSA improves protein function prediction by integrating functional labels during embedding, outperforming existing methods in yeast and human networks.

The paper evaluates methods for imputing drug connectivity mapping with incomplete data, finding that neighborhood-based collaborative filtering performs best when cell type is considered.

The paper uses single-cell RNA sequencing to compare fetal brain microglia and Hofbauer cells, revealing shared transcriptional programs and sex-specific differences.

The paper evaluates module identification methods across disease-relevant networks using GWAS data to identify top-performing algorithms and disease-associated modules.

TEMPO detects pathway-specific temporal dysregulation in disease by analyzing combined temporal data from multiple individuals.

The paper identifies functional mediators of pulmonary diseases by analyzing pathway centrality in protein interaction networks, finding shared and disease-specific pathways involved in inflammation and other processes.