Machine Learning Detection of Microtubule and Swarms Gliding on 2D Kinesin Substrate, Dataset and Models
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2025
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| _version_ | 1866901483515019264 |
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| author | Singh Grewal, Sidak Kawamata, Ibuki |
| author_facet | Singh Grewal, Sidak Kawamata, Ibuki |
| contents | <p>This dataset accompanies the study "Machine Learning Detection of Microtubule and Swarms Gliding on 2D Kinesin Substrate" It contains the experimental fluorescent detected labelled data and trained machine-learning models used for segmentation.</p> <p>Microtubule motility assays on kinesin-coated substrates are widely used to investigate emergent behaviours in active matter. Traditional analysis methods rely on manual extraction of microtubule trajectories, limiting reproducibility and scalability. To address this challenge, we developed an automated image-analysis framework based on deep-learning–driven segmentation and tracking. The computational code for this pipeline is openly available on GitHub (link in the “Related Resources” section), while this Zenodo repository provides all <strong>datasets and trained models necessary to reproduce the key results</strong>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15640817 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Machine Learning Detection of Microtubule and Swarms Gliding on 2D Kinesin Substrate, Dataset and Models Singh Grewal, Sidak Kawamata, Ibuki Microtubules Microtubule Swarms Segmentation Model <p>This dataset accompanies the study "Machine Learning Detection of Microtubule and Swarms Gliding on 2D Kinesin Substrate" It contains the experimental fluorescent detected labelled data and trained machine-learning models used for segmentation.</p> <p>Microtubule motility assays on kinesin-coated substrates are widely used to investigate emergent behaviours in active matter. Traditional analysis methods rely on manual extraction of microtubule trajectories, limiting reproducibility and scalability. To address this challenge, we developed an automated image-analysis framework based on deep-learning–driven segmentation and tracking. The computational code for this pipeline is openly available on GitHub (link in the “Related Resources” section), while this Zenodo repository provides all <strong>datasets and trained models necessary to reproduce the key results</strong>.</p> |
| title | Machine Learning Detection of Microtubule and Swarms Gliding on 2D Kinesin Substrate, Dataset and Models |
| topic | Microtubules Microtubule Swarms Segmentation Model |
| url | https://doi.org/10.5281/zenodo.15640817 |