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.
Paper Yale Single Cell Symposium 2022 Manuscript in Revision
Beta cells are essential drivers of pancreatic ductal adenocarcinoma development
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.
Paper Code GSP 2023 Manuscript Published
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.
Paper Code SAMPTA 2023 Manuscript Published
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.
Paper Code LoG 2023 Manuscript Published
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.
Paper Blog Post Code ISMB 2021 Immunology 2023 Manuscript Published
Mapping the gene space at single-cell resolution with gene signal pattern analysis
Revealing dynamic temporal regulatory networks driving cancer cell state plasticity with neural ODE-based optimal transport
TrajectoryNet learns continuous and cell-specific trajectories from longitudinal single-cell data.

A Tong*, M Kuchroo*, S Gupta, A Venkat,…,CL Chaffer, S Krishnaswamy. In Review.
Paper Code Manuscript in Review
Revealing dynamic temporal regulatory networks driving cancer cell state plasticity with neural ODE-based optimal transport