ReviewComputational and structural biotechnology journal2023
Recent development of computational cluster analysis methods for single-molecule localization microscopy images.
Review in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 26 citations in OpenAlex.
- Adversarial Erasing Enhanced Multiple Instance Learning (siMILe): Discriminative Identification of Oligomeric Protein Structures in Single Molecule Localization Microscopy.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2026Article
- Modern RNA Quantification Methods: From RT-qPCR to Advanced Microscopy.The journal of physical chemistry. B · 2026Review
- Super-resolution mapping reveals NAD⁺-delivering probiotic extracellular vesicles as nanotherapeutics for organelle protection and inflammation control.Journal of nanobiotechnology · 2026Article
- Case Study: Illuminating the Nanoscale World of Microbiology.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Revealing plasma membrane protein dynamics in living plant cells with single-molecule tracking.Journal of experimental botany · 2025Article
- Filamentation-driven peripheral clustering of the inducible lysine decarboxylase is crucial for E. coli acid stress response.Communications biology · 2025Article
- Machine learning-based prediction of preterm birth risk using methylation changes in neonatal cord blood CpG sites.BMC pregnancy and childbirth · 2025Article
- Closing the multichannel gap through computational reconstruction of interaction in super-resolution microscopy.Patterns (New York, N.Y.) · 2025Review
- SuperResNET: Model-Free Single-Molecule Network Analysis Software Achieves Molecular Resolution of Nup96.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2025Article
- Integrating bioengineering, super-resolution microscopy and mechanobiology in autophagy research: addendum to the guidelines (4th edition).Autophagy · 2025Article
- Model-based evaluation of connexin hemichannel permeability.Computational and structural biotechnology journal · 2025Article
- Cluster parameter-based DBSCAN maps for image characterization.Computational and structural biotechnology journal · 2025Article
- AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth.The Journal of cell biology · 2024Review
- SEMORE: SEgmentation and MORphological fingErprinting by machine learning automates super-resolution data analysis.Nature communications · 2024Article
- Edge roughness analysis in nanoscale for single-molecule localization microscopy images.Nanophotonics (Berlin, Germany) · 2024Article
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Authors and funding
2 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
With the development of super-resolution imaging techniques, it is crucial to understand protein structure at the nanoscale in terms of clustering and organization in a cell. However, cluster analysis from single-molecule localization microscopy (SMLM) images remains challenging because the classical computational cluster analysis methods developed for conventional microscopy images do not apply to pointillism SMLM data, necessitating the development of distinct methods for cluster analysis from SMLM images. In this review, we discuss the development of computational cluster analysis methods for SMLM images by categorizing them into classical and machine-learning-based methods. Finally, we address possible future directions for machine learning-based cluster analysis methods for SMLM data.
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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.