Topological and Geometric Data Analysis

XplainTDA: Explaining the topological structure of single-cell multi-omics data through perturbation

XplainTDA identifies molecular determinants of topological structures in single-cell multi-omics data through perturbation-based analysis.

Minkowski Tensors as a Lightweight and Interpretable Representation for Three-Dimensional Morphology

This paper introduces Minkowski Tensors as interpretable 3D shape descriptors, showing competitive performance on biomedical image classification tasks.

Topologically-based parameter inference for agent-based model selection from spatiotemporal cellular data

The paper introduces TOPAZ, a framework that uses topological data analysis and Bayesian methods to infer agent-based models from spatiotemporal cellular data, showing it can accurately recover model parameters and distinguish between models.

An Enhanced Pipeline for Multi-Omic Integration Based on Topological Data Analysis

The paper introduces an enhanced pipeline for multi-omic integration using topological data analysis to rank genes by disease relevance, validated on TCGA breast cancer data.

An interpretable deep learning framework for classifying neuronal morphologies using topology and graph neural networks

This paper presents a deep learning framework that classifies neuronal morphologies using topology and graph neural networks, achieving accuracy comparable to human experts.

Scalable Extraction of Information on Protein-Protein Interactions using Topological Data Analysis

This paper introduces a scalable topological data analysis framework for predicting protein-protein interaction interfaces, achieving comparable performance to a geometric deep learning method with reduced computational cost.

Temporal topology provides an interpretable framework for neuronal morphogenesis

The paper introduces TTMD, a framework using temporal topology to analyze neuronal morphogenesis, showing it can decode developmental stages and mutations in Drosophila neurons.

Automated Quantification of Stereotypical Motor Movements in Autism Using Persistent Homology

This paper develops a method using persistent homology to automate the quantification of stereotypical motor movements in autism, showing it generalizes across individuals.

Emergent topological structure in spontaneous brain-organoid activity

This study uses persistent homology to analyze neural activity in brain organoids and finds structured topological features at the scale of typical recordings.

MOLA: a novel topological data analysis framework for analyzing multiomic loops in precision medicine

MOLA identifies multiomic loops in datasets, showing better capture of epigenetic alterations in Influenza A virus data and associations with progression in breast cancer.

Topological data analysis captures complex behavioral dynamics during naturalistic social interaction between domestic ferrets

This study uses Temporal Mapper to analyze postural dynamics during naturalistic social interaction in ferrets, finding more complex and consistent state transitions during social activity.

The shape of evolution: persistent homology of genetic-distancedata as an observable of reticulate processes in pathogen andplant genomes

This paper introduces persistent homology to analyze genetic-distance data, showing it can detect reticulate evolutionary processes in pathogens and plants.

Spatial Analysis of Malignant-Immune Cell Interactions in the Tumor Microenvironment Using Topological Data Analysis

This study introduces a topological data analysis framework to analyze malignant-immune cell interactions in the tumor microenvironment using high-resolution imaging data.

Topological Pharmacokinetics: Reading the Shape of Drug Disposition from Data

This paper introduces Topological Pharmacokinetics (TPK), a framework that extracts shape-based pharmacokinetic features from concentration-time curves using topological methods, validated on simulated data.

Topology-driven classification of time series

This paper introduces a geometric framework for time series classification using delay embeddings and topology, achieving state-of-the-art performance on EEG data without feature engineering.

Kernel Matrix Completion with Topological and Spectral Features for Multi-Modal Classification

The paper develops a kernel matrix completion method integrating topological and spectral features to classify yeast proteins, achieving improved ROC performance on incomplete data.

The Euler Characteristic Transform Enables Classification of Complex Plant Shapes and Prediction of Leaf Venation from Blade Geometry

This paper introduces the Euler Characteristic Transform (ECT) for classifying plant shapes and predicting leaf venation from blade geometry using deep learning.

Topological Analysis of Multi-Network Threading in the Pancreas

The paper uses topological data analysis to study loop structures in pancreatic networks, revealing distinct developmental timelines and spatial entanglement patterns.

Topological Data Analysis of Spatial Protein Expression in Multiplexed Spatial Proteomics Studies

The paper introduces TOASTER, a method that uses topological data analysis to associate spatial protein expression with patient outcomes without relying on cell segmentation or phenotyping.

Detecting Clinically Relevant Topological Structures in Multiplexed Spatial Proteomics Imaging Using TopKAT

This paper introduces TopKAT, a topology-based method to detect associations between spatial cell structures and clinical outcomes in spatial proteomics data.

TopoTome: Topology-informed unsupervised segmentation and analysis of 3D images

TopoTome is an unsupervised 3D image segmentation algorithm that outperforms existing methods by using topological data analysis to detect and segment structural features in biological images.

DeepAtlas: a tool for effective manifold learning

DeepAtlas generates local manifold embeddings and assesses the manifold hypothesis, finding that many datasets, including single-cell RNA-sequencing, do not conform to it.

Impact of integration on persistent homology clustering and biological signal detection in scRNA-seq data

This paper evaluates how data integration affects persistent homology clustering and biological signal detection in scRNA-seq data, showing topological methods can help distinguish biological patterns from batch noise.

Topological Data Analysis of Protein Structure Manifolds from Molecular Dynamics Computer Simulation

This paper uses topological data analysis to study protein structure dynamics from molecular simulations, showing how mutations affect the topology of protein structure manifolds.

Multiple neural network approaches, including use of topological data analysis, enhances classification of human induced pluripotent stem cell colonies by treatment condition

The paper uses neural networks with topological data analysis to classify human induced pluripotent stem cell colonies by treatment condition, achieving performance comparable to ResNet.

The Topology of Representational Geometry

This paper introduces a method that enhances representational similarity analysis by incorporating computational topology to better capture the shape of neural representational spaces.

MesoSCOUT: A novel tool for revealing mesoscale organization in the white-matter connectome

MesoSCOUT uses persistent homology to analyze white matter connectivity differences between schizophrenia and healthy controls, revealing mesoscale structural patterns across datasets.

Combined topological data analysis and geometric deep learning reveal niches by the quantification of protein binding pockets

This paper combines topological data analysis and geometric deep learning to analyze protein pockets and show that integrating local and global representations improves enzyme classification.

Skeletonization of neuronal processes using Discrete Morse techniques from computational topology

The paper introduces a method combining deep learning and Discrete Morse techniques to skeletonize neuronal processes from tracer injection data, improving the accuracy of axon length density estimation.

Topological data analysis of pattern formation of human induced pluripotent stem cell colonies

This paper introduces a computational pipeline using topological data analysis to quantify multicellular patterning in human induced pluripotent stem cell colonies, revealing subtle differences in differentiation stages.