1 citations · 2 across the 6 of their papers we have counts for
6 papers
scShapeBench: Discovering geometry from high dimensional scRNAseq data
Andrew J Steindl, João Felipe Rocha, Brian Tshilengi Di Bassinga +13
High-dimensional point cloud data arise across many scientific domains, especially single-cell biology. The shapes or topologies of these datasets determine the types of informatio…
MIOFlow 2.0: A unified framework for inferring cellular stochastic dynamics from single cell and spatial transcriptomics data
Xingzhi Sun, João Felipe Rocha, Brett Phelan +11
Understanding cellular trajectories via time-resolved single-cell transcriptomics is vital for studying development, regeneration, and disease. A key challenge is inferring continu…
STAGED: A Multi-Agent Neural Network for Learning Cellular Interaction Dynamics
Joao F. Rocha, Ke Xu, Xingzhi Sun +6
The advent of single-cell technology has significantly improved our understanding of cellular states and subpopulations in various tissues under normal and diseased conditions by e…
HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
Hiren Madhu, João Felipe Rocha, Tinglin Huang +3
Single-cell transcriptomics and proteomics have become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellul…
ProtScape: A molecular structure and energy-aware representation for protein conformation generation
Siddharth Viswanath, Dhananjay Bhaskar, Xingzhi Sun +13
Molecular dynamics (MD) simulations are a principled but computationally expensive approach for studying protein conformational variability, making it challenging to generate large…
Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics
Xingzhi Sun, Charles Xu, João F. Rocha +6
In many data-driven applications, higher-order relationships among multiple objects are essential in capturing complex interactions. Hypergraphs, which generalize graphs by allowin…