ArticleScience advances2019
A trainable clustering algorithm based on shortest paths from density peaks.
Article in Science advances, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Systematic analysis of immune cell motility leveraging the open intravital microscopy database Immunemap.The EMBO journal · 2026Article
- From microscopes to maps: enabling a new era of open and reproducible data sharing in intravital imaging.The EMBO journal · 2026Article
- Understanding the Impact of Evaluation Metrics in Kinetic Models for Consensus-Based Segmentation.Entropy (Basel, Switzerland) · 2025Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Optimization of Density Peak Clustering Algorithm Based on Improved Black Widow Algorithm.Biomimetics (Basel, Switzerland) · 2023Article
- Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity.Nature communications · 2022Article
- Multiphoton intravital microscopy of rodents.Nature reviews. Methods primers · 2022Article
- Characterization of the Dynamic Behavior of Neutrophils Following Influenza Vaccination.Frontiers in immunology · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clustering is a technique to analyze empirical data, with a major application for biomedical research. Essentially, clustering finds groups of related points in a dataset. However, results depend on both metrics for point-to-point similarity and rules for point-to-group association. Non-appropriate metrics and rules can lead to artifacts, especially in case of multiple groups with heterogeneous structure. In this work, we propose a clustering algorithm that evaluates the properties of paths between points (rather than point-to-point similarity) and solves a global optimization problem, finding solutions not obtainable by methods relying on local choices. Moreover, our algorithm is trainable. Hence, it can be adapted and adopted for specific datasets and applications by providing examples of valid and invalid paths to train a path classifier. We demonstrate its applicability to identify heterogeneous groups in challenging synthetic datasets, segment highly nonconvex immune cells in confocal microscopy images, and classify arrhythmic heartbeats in electrocardiographic signals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.