1 citations · 1 across the 11 of their papers we have counts for
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SPINET: Sheaf Protein Inverse Folding Network
Jens Lundsgaard, Colin Mikulski, Zhixuan Yan +1
Proteins change shape as they function, yet most inverse folding models predict amino acid sequences from a single, fixed backbone. A central challenge in protein engineering is to…
T-SNN: Temporal Simplicial Neural Network for EEG Decoding
Nikita Malik, Shubhajit Roy, Mohit Kataria +4
Decoding brain states requires models that capture both the evolution of neural activity and interactions among groups of brain regions. Existing EEG methods often treat recordings…
BrainDyn: A Sheaf Neural ODE for Forecasting and Generating Brain Dynamics
Siddharth Viswanath, Panayiotis Ketonis, Chen Liu +11
Brain activity unfolds as coordinated patterns across regions over time, and modeling these dynamics is a central goal in computational neuroscience. Because activity is distribute…
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…