Topological and Geometric Data Analysis

This paper introduces a topological data analysis framework to model microbiome dynamics, identifying state transitions linked to health and environmental processes.

The paper applies the TopS method to flow cytometry data to identify rare cell populations in a human hematopoietic stem cell dataset.

This paper introduces a topological data analysis classifier for repeated measurements, achieving high accuracy in two case studies and demonstrating its utility as a feature selection tool.

Hyppo-X is a framework for exploring complex phenomics data to extract hypotheses about environmental influences on phenotypic traits in plants.

The paper introduces a topological data analysis method for analyzing single molecule localization microscopy data, improving segmentation and quantifying nano-structure.

This study identifies topological phase transitions in functional brain networks using TDA, showing they occur when Euler entropy has a singularity and correlate with multidimensional topological holes.

The paper uses algebraic topology to classify artificial neuronal network dynamics by analyzing topological features from spike-train distances, showing improved classification accuracy with multiple distance measures.

This study investigates whether persistent homology can recover topological structure from event-related fMRI data under practical constraints.

This paper introduces a topological persistence-based framework for comparing neuronal tree structures, showing it captures more information than traditional morphometric summaries.

The paper introduces a framework for analyzing paired brain networks using persistent homology and sparse learning, demonstrating its application to twin fMRI data to assess heritability significance.