1 citations · 1 across the 9 of their papers we have counts for
9 papers
Structure Learning on Clustered Data
Ryan Thompson, Matt P. Wand, Veerabhadran Baladandayuthapani
Recent algorithmic advances have made directed acyclic graph (DAG) structure learning scalable for causal discovery. Yet, the currently available techniques assume a completely hom…
MoSAIC: Multi-Resolution Spatial Regression Analysis of Cellular Colocalizations in Cancer Imaging
Jessica Aldous, Michele Peruzzi, Maria Masotti +4
Hierarchical multiplex imaging approaches generate spatially resolved single-cell measurements across multiple, spatially organized fields of view (FOVs) within patient tumor speci…
Multi-resolution Spatial Graphical Regression Models for Hierarchical Spatial Transcriptomics Data
Liying Chen, Satwik Acharyya, Allison M. May +3
Advances in spatial transcriptomics (ST) technologies enable systematic molecular characterization of tumor microenvironment, tumor gradients and gene regulatory networks. Cancer p…
A Time-Varying and Covariate-Dependent Correlation Model for Multivariate Longitudinal Studies
Qingzhi Liu, Gen Li, Anastasia K. Yocum +3
In multivariate longitudinal studies, associations between outcomes often exhibit time-varying and individual level heterogeneity, motivating the modeling of correlations as an exp…
TopSpace: spatial topic modeling for unsupervised discovery of multicellular spatial tissue structures in multiplex imaging
Junsouk Choi, Jian Kang, Veerabhadran Baladandayuthapani
Motivation: Understanding the spatial architecture of tissues is essential for decoding the complex interactions within cellular ecosystems and their implications for disease patho…
Statistical Analysis of Quantitative Cancer Imaging Data
Shariq Mohammed, Maria Masotti, Nathaniel Osher +2
Recent advances in types and extent of medical imaging technologies has led to proliferation of multimodal quantitative imaging data in cancer. Quantitative medical imaging data re…
Spatially Structured Regression for Non-conformable Spaces: Integrating Pathology Imaging and Genomics Data in Cancer
Nathaniel Osher, Jian Kang, Arvind Rao +1
The spatial composition and cellular heterogeneity of the tumor microenvironment plays a critical role in cancer development and progression. High-definition pathology imaging of t…
Geometry-driven Bayesian Inference for Ultrametric Covariance Matrices
Tsung-Hung Yao, Zhenke Wu, Karthik Bharath +1
Ultrametric matrices are a class of covariance matrices that arise in latent tree models. As a parameter space in a statistical model, the set of ultrametric matrices is neither co…
Probabilistic Learning of Treatment Trees in Cancer
Tsung-Hung Yao, Zhenke Wu, Karthik Bharath +2
Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translationa…