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Cluster analysis for localisation-based data sets: dos and don'ts when quantifying protein aggregates.
- Published in:
- Frontiers in Bioinformatics, 2023, p. 01, doi. 10.3389/fbinf.2023.1237551
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Machine learning for cluster analysis of localization microscopy data.
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- Nature Communications, 2020, v. 11, n. 1, p. 1, doi. 10.1038/s41467-020-15293-x
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An agent-based model of molecular aggregation at the cell membrane.
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- PLoS ONE, 2020, v. 15, n. 2, p. 1, doi. 10.1371/journal.pone.0226825
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- Article
Dynamic Bayesian Cluster Analysis of Live‐Cell Single Molecule Localization Microscopy Datasets.
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- Small Methods, 2018, v. 2, n. 9, p. 1, doi. 10.1002/smtd.201800008
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Quantification of fibrous spatial point patterns from single-molecule localization microscopy (SMLM) data.
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- Bioinformatics, 2017, v. 33, n. 11, p. 1703, doi. 10.1093/bioinformatics/btx026
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Superresolution imaging of the cytoplasmic phosphatase PTPN22 links integrin-mediated T cell adhesion with autoimmunity.
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- Science Signaling, 2016, v. 9, n. 448, p. 1, doi. 10.1126/scisignal.aaf2195
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- Article
Topographic prominence as a method for cluster identification in single-molecule localisation data.
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- Journal of Biophotonics, 2015, v. 8, n. 11/12, p. 925, doi. 10.1002/jbio.201400127
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- Article
Bayesian cluster identification in single-molecule localization microscopy data.
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- Nature Methods, 2015, v. 12, n. 11, p. 1072, doi. 10.1038/nmeth.3612
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