Suzanne Sindi

This paper develops an ODE-based GRN inference method using dynamic transcriptional data and validates it on synthetic yeast and Candida albicans GRNs.

scANNA is an interpretable deep learning model for scRNA-seq that uses neural attention to learn gene associations, enabling downstream analyses without retraining.

This paper reviews deep learning methods for spatial transcriptomics, highlighting challenges and opportunities in analyzing spatially-resolved gene expression data.

N-ACT is an interpretable deep learning model for automatic cell type identification that detects salient genes, outperforming conventional methods in accuracy and scalability.

ACTIVA generates realistic synthetic scRNA-seq data with cell-type identification, improving classifier performance and reducing training time compared to existing methods.

The paper uses a mathematical model of coagulation under flow to identify factor V as a modifier of thrombin generation in hemophilia A, showing its interaction with factor VIII.

The paper introduces MultiCellDS, a standard and community-driven initiative for sharing multicellular data to enhance research in cell biology.

The paper introduces MultiCellDS, a standard for multicellular data curation, and presents MultiCellDB as a database to store associated data, enhancing data sharing and utility in biological and clinical research.