5 papers
Empirically Calibrated Conditional Independence Tests
Milleno Pan, Antoine de Mathelin, Wesley Tansey
Conditional independence tests (CIT) are widely used for causal discovery and feature selection. Even with false discovery rate (FDR) control procedures, they often fail to provide…
Scalable Causal Structure Learning via Amortized Conditional Independence Testing
James Leiner, Brian Manzo, Aaditya Ramdas +1
Controlling false positives (Type I errors) through statistical hypothesis testing is a foundation of modern scientific data analysis. Existing causal structure discovery algorithm…
Distilled Protein Backbone Generation
Liyang Xie, Haoran Zhang, Zhendong Wang +2
Diffusion- and flow-based generative models have recently demonstrated strong performance in protein backbone generation tasks, offering unprecedented capabilities for de novo prot…
A Hierarchical Variational Graph Fused Lasso for Recovering Relative Rates in Spatial Compositional Data
Joaquim Valerio Teixeira, Ed Reznik, Sudpito Banerjee +1
The analysis of spatial data from biological imaging technology, such as imaging mass spectrometry (IMS) or imaging mass cytometry (IMC), is challenging because of a competitive sa…
Controllable diffusion-based generation for multi-channel biological data
Haoran Zhang, Mingyuan Zhou, Wesley Tansey
Spatial profiling technologies in biology, such as imaging mass cytometry (IMC) and spatial transcriptomics (ST), generate high-dimensional, multi-channel data with strong spatial…