Daniel B. Burkhardt

The paper presents a longitudinal multimodal benchmarking dataset and competition to evaluate modality prediction methods for gene regulatory inference, showing top models outperform existing approaches.

AAnet identifies archetypal cell states in single-cell data to reveal tumor heterogeneity and spatial organization.

A NeurIPS 2021 benchmarking competition evaluated over 2600 submissions from 280 competitors across three multimodal single-cell integration tasks using a purpose-built benchmarking dataset and cloud evaluation pipeline.

The paper examines the role of the human-specific enhancer HACNS1 in human evolution by analyzing its effects on gene expression in humanized mice.

The paper introduces FMGAN, a conditional GAN that predicts hard-to-obtain biological information from easy-to-obtain data, demonstrating applications in drug discovery and clinical outcomes.

This paper introduces a method to quantify perturbation effects at single-cell resolution using manifold-based likelihood estimation and vertex frequency clustering, achieving higher accuracy than existing methods.

The paper introduces Multiscale PHATE, a method for exploring high-dimensional biomedical data, and applies it to identify immune cell signatures associated with severe COVID-19 outcomes.

The paper introduces PHATE, a visualization method that captures nonlinear structure in biological data, showing it preserves patterns better than existing tools and reveals novel insights in scRNA-seq data.