Guy Wolf

GeoSinkhorn Flow: Geometry-Aware Flow Matching for Perturbation-Conditioned Dynamics in Single cell Data Phenoscapes

GeoSinkhorn Flow converts static phenoscapes into dynamic ones by learning perturbation-conditioned transport trajectories that respect the phenoscape's geometry and generalize to unseen conditions.

Mapping Alzheimer's neuropathology signatures to the whole brain transcriptome using machine learning data fusion

Cell type-specific logistic regression estimators trained on 427 ROSMAP prefrontal donors project Braak- and β-amyloid-like transcriptomic signatures onto the 108-region Siletti atlas, flagging brainstem, hypothalamic and rostral hippocampal regions.

Gaining Biological Insights through Supervised Data Visualization

The paper introduces RF-PHATE, a supervised data visualization method that integrates expert knowledge to enhance interpretation of complex biological data.

Geometry aware graph attention networks to explain single-cell chromatin state and gene expression

SEAGALL uses geometry-aware graph attention networks to extract biologically relevant features from single-cell data, revealing cell identity drivers beyond marker genes.

Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization

This paper uses artificial neural networks to study single-neuron adaptation mechanisms and finds they improve robustness and recover biological coding strategies like gain scaling.

Multi-view manifold learning of human brain statetrajectories

This paper introduces T-PHATE, a nonlinear manifold learning algorithm for time-series data like fMRI, showing improvements in visualization, classification, and segmentation.

Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity

Neurospectrum extracts interpretable spatiotemporal features from neural activity using a geometric and topological deep learning framework, outperforming traditional methods in simulations and fMRI data.

Data-driven approaches for genetic characterization of SARS-CoV-2 lineages

The paper presents computational tools for analyzing SARS-CoV-2 genetic data, showing that haplotype networks and unsupervised learning can reveal lineage emergence and population dynamics.

Structural and developmental principles of neuropil assembly in C. elegans

The paper uses diffusion condensation to analyze the C. elegans nerve ring neuropil, revealing its stratified structure and developmental principles.

Quantifying the effect of experimental perturbations at single-cell resolution

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.

Topological analysis of single-cell data reveals shared glial landscape of macular degeneration and neurodegenerative diseases

A topological machine learning approach applied to single-nucleus RNA sequencing identifies shared glial activation states in AMD, AD, and MS.

Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

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.

Embedding single-cell experimental conditions to reveal manifold structure of cancer drug perturbation effects

This paper introduces PhEMD, a method to embed drug perturbation effects in single-cell experiments, revealing low-dimensional manifold structures and enabling efficient drug effect inference.

Visualizing Structure and Transitions for Biological Data Exploration

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.

Exploring Single-Cell Data with Deep Multitasking Neural Networks

SAUCIE is a deep neural network that processes large single-cell datasets, performs batch correction, and identifies cluster-based signatures in dengue patient T cells.

MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data

MAGIC improves single-cell RNA-seq data by imputing missing values and revealing gene-gene interactions through a diffusion-based method.