Biological prior knowledge

Beta cells are essential drivers of pancreatic ductal adenocarcinoma development

Disentangling the origin of β cells misexpressing pro-tumorigenic hormones in the context of obesity and other stressors.

C Garcia*, A Venkat*, DC McQuaid*,…,S Krishnaswamy, MD Muzumdar. In Revision.

Directed Scattering for Knowledge Graph-based Cellular Signaling Analysis

DSAE employs a directed version of a geometric scattering transform for embedding nodes of directed graphs.

A Venkat*, J Chew*, F Cardoso Rodriguez, CJ Tape, M Perlmutter, S Krishnaswamy. ICASSP 2024.

Directed Scattering for Knowledge Graph-based Cellular Signaling Analysis

Graph Fourier MMD for Signals on Graphs

GFMMD embeds signals defined on graphs via an optimal witness function.

S Leone, A Tong, G Huguet, A Venkat, G Wolf, S Krishnaswamy. SampTA 2023.

Graph Fourier MMD for Signals on Graphs

Inferring dynamic regulatory interaction graphs from time series data with perturbations

RiTINI infers time-varying interaction graphs to predict casual behavior of a system. D Bhaskar, S Magruder, E De Brouwer, A Venkat, F Wenkel, G Wolf, S Krishnaswamy. LoG Conference 2023.

Inferring dynamic regulatory interaction graphs from time series data with perturbations

Mapping the gene space at single-cell resolution with gene signal pattern analysis

GSPA learns gene-gene relationships from single-cell data by embedding the gene space.

A Venkat, S Leone, S Youlten, E Fagerberg, J Attanasio, NS Joshi, S Krishnaswamy. Nature Computational Science 2024.

Mapping the gene space at single-cell resolution with gene signal pattern analysis

Revealing dynamic temporal trajectories and underlying regulatory networks with Cflows

Cflows learns continuous and cell-specific trajectories from longitudinal single-cell data.

X Sun*, S Gupta*, A Tong*, M Kuchroo*, D Bhaskar, C Liu, A Venkat,…,CL Chaffer, S Krishnaswamy. In Review.