Ethan Weinberger

An Explainable AI Framework for Identifying Universal Aging Signatures in Cell Embeddings

ACE identifies universal aging signatures across species by disentangling aging-related gene expression from other biological variations using explainable AI.

A deep generative model for capturing cell to phenotype relationships

The paper introduces milVI, a deep generative model that infers missing phenotype labels and connects cell-level data to donor-level phenotypes in single-cell studies.

Isolating structured salient variations in single-cell transcriptomic data with StrastiveVI

StrastiveVI isolates aging and Alzheimer's disease-associated patterns in scRNA-seq data by separating them from sex, tissue, and cell type variations.

Isolating salient variations of interest in single-cell data with contrastiveVI

The paper introduces contrastiveVI, a method to disentangle shared and treatment-specific variations in scRNA-seq data, showing improved performance over existing methods in four datasets.

Disentangling shared and group-specific variations in single-cell transcriptomics data with multiGroupVI

The paper introduces multiGroupVI, a deep generative model for analyzing grouped scRNA-seq data, and shows it outperforms existing methods on simulated data and identifies regional differences in mouse small intestine data.

Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer's disease neuropathologies

The paper introduces MD-AD, a deep learning framework that identifies non-linear gene expression-neuropathology relationships in Alzheimer's disease using multi-cohort data, revealing sex-specific immune response patterns.

Transferable representations of single-cell transcriptomic data

This paper introduces HD-AE, a deep autoencoder that learns transferable representations of scRNA-seq data across datasets, enabling comparison without retraining.