Bianca Dumitrascu

This paper introduces waxMorph, a differentiable framework for simulating and reconstructing 3D morphogenesis, showing it can infer shape programs and trajectories from biological data.

This paper introduces STERN, a framework that quantitatively compares developmental trajectories across embryos by learning spatiotemporal representations from 4D imaging data.

The paper uses morphodynamic features from live imaging to predict cell fate during Xenopus mucociliary epithelial differentiation, showing that integrated time-resolved features outperform single features.

Wayfarer analyzes spatial -omics data across multiple scales to reveal how spatial patterns change with resolution, showing shifts in tumor progression.

The paper develops a computational framework to jointly analyze transcriptional and mechanical signals in spatial transcriptomics data from mouse embryos, identifying predictive signatures of tissue compartment boundaries.

The paper develops a method to engineer synthetic cis-regulatory elements that control gene expression in response to signals, showing they can be designed using quantitative rules.

Patches decodes shared and condition-specific transcriptional programs in wound healing using scRNA-seq data, revealing universal patterns and treatment-specific changes.

This study shows that adult zebrafish can regenerate functional synovial joints after complete removal, revealing a multipotent cell source and regenerative programs.

Genes2Genes aligns single-cell trajectories using a Bayesian DP framework, revealing gene-level mismatches and highlighting differences in T cell differentiation between in vitro and in vivo states.

HYFA improves multi-tissue gene expression imputation by leveraging hypergraphs and transfer learning, outperforming existing methods and enabling cell-type signature resolution from bulk data.

The paper develops a computational framework for spatial omics power analysis using in silico tissue generation and applies it across diverse spatial data modalities.

scGene-Fit selects optimal gene markers for cell type discrimination in single-cell RNA-seq data by optimizing label recovery with compressive classification methods.

The paper introduces BETS, a statistical framework for inferring gene regulatory networks from transcriptional time series data, and validates it using glucocorticoid response data and external sources.

netNMF-sc improves scRNA-seq analysis by imputing dropout events and reducing dimensionality using gene-gene interactions, outperforming existing methods at high dropout rates.

GT-TS is a computational method that optimizes cell type discovery in multi-tissue scRNA-seq by iteratively planning data collection.