99 citations · 128 across the 12 of their papers we have counts for
14 papers
JASPR: Joint Spatial Representation learning of histology and spatial genomics for improved virtual genomic screening and clinical prognostication
Marija Pizurica, Eric Zimmermann, Neil Tenenholtz +5
Recent studies have shown that spatial properties of tumors are critical for understanding disease biology and predicting patient outcomes. These spatial properties are increasingl…
MorphoHELM: A Comprehensive Benchmark for Evaluating Representations for Microscopy-Based Morphology Assays
Emre Hayir, Lorin Crawford, Alex X. Lu
Microscopy images contain rich information about how cells respond to perturbations, making them essential to applications like drug screening. To quantify images, researchers ofte…
Multimodal Alignment Improves Generalizability of Genomic Biomarker Prediction in Computational Pathology
Ekaterina Redekop, Eric Zimmermann, Ava P Amini +5
Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for t…
A Large-Scale Benchmark of Cross-Modal Learning for Histology and Gene Expression in Spatial Transcriptomics
Rushin H. Gindra, Giovanni Palla, Mathias Nguyen +6
Spatial transcriptomics enables simultaneous measurement of gene expression and tissue morphology, offering unprecedented insights into cellular organization and disease mechanisms…
BetaExplainer: A Probabilistic Method to Explain Graph Neural Networks
Whitney Sloneker, Shalin Patel, Michael Wang +2
Graph neural networks (GNNs) are powerful tools for conducting inference on graph data but are often seen as "black boxes" due to difficulty in extracting meaningful subnetworks dr…
Statistical Inference on Grayscale Images via the Euler-Radon Transform
Kun Meng, Mattie Ji, Jinyu Wang +4
Tools from topological data analysis have been widely used to represent binary images in many scientific applications. Methods that aim to represent grayscale images (i.e., where p…
Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations
Hammaad Adam, Fan Yin, Huibin +5
Randomized experiments often need to be stopped prematurely due to the treatment having an unintended harmful effect. Existing methods that determine when to stop an experiment ear…
A Simple Approach for Local and Global Variable Importance in Nonlinear Regression Models
Emily T. Winn-Nuñez, Maryclare Griffin, Lorin Crawford
The ability to interpret machine learning models has become increasingly important as their usage in data science continues to rise. Most current interpretability methods are optim…
Generalizing Variational Autoencoders with Hierarchical Empirical Bayes
Wei Cheng, Gregory Darnell, Sohini Ramachandran +1
Variational Autoencoders (VAEs) have experienced recent success as data-generating models by using simple architectures that do not require significant fine-tuning of hyperparamete…
Interpreting Deep Neural Networks Through Variable Importance
Jonathan Ish-Horowicz, Dana Udwin, Seth Flaxman +2
While the success of deep neural networks (DNNs) is well-established across a variety of domains, our ability to explain and interpret these methods is limited. Unlike previously p…
Variable Prioritization in Nonlinear Black Box Methods: A Genetic Association Case Study
Lorin Crawford, Seth R. Flaxman, Daniel E. Runcie +1
The central aim in this paper is to address variable selection questions in nonlinear and nonparametric regression. Motivated by statistical genetics, where nonlinear interactions…
Tropical Sufficient Statistics for Persistent Homology
Anthea Monod, Sara Kališnik, Juan Ángel Patiño-Galindo +1
We show that an embedding in Euclidean space based on tropical geometry generates stable sufficient statistics for barcodes. In topological data analysis, barcodes are multiscale s…
Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis
Lorin Crawford, Anthea Monod, Andrew X. Chen +2
Glioblastoma multiforme (GBM) is an aggressive form of human brain cancer that is under active study in the field of cancer biology. Its rapid progression and the relative time cos…
Bayesian Approximate Kernel Regression with Variable Selection
Lorin Crawford, Kris C. Wood, Xiang Zhou +1
Nonlinear kernel regression models are often used in statistics and machine learning because they are more accurate than linear models. Variable selection for kernel regression mod…