Outlier Removal in Cryo-EM via Radial Profiles
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929492422819840 |
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| author | Kapnulin, Lev Heimowitz, Ayelet Sharon, Nir |
| author_facet | Kapnulin, Lev Heimowitz, Ayelet Sharon, Nir |
| contents | The process of particle picking, a crucial step in cryo-electron microscopy (cryo-EM) image analysis, often encounters challenges due to outliers, leading to inaccuracies in downstream processing. In response to this challenge, this research introduces an additional automated step to reduce the number of outliers identified by the particle picker. The proposed method enhances both the accuracy and efficiency of particle picking, thereby reducing the overall running time and the necessity for expert intervention in the process. Experimental results demonstrate the effectiveness of the proposed approach in mitigating outlier inclusion and its potential to enhance cryo-EM data analysis pipelines significantly. This work contributes to the ongoing advancement of automated cryo-EM image processing methods, offering novel insights and solutions to challenges in structural biology research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05523 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Outlier Removal in Cryo-EM via Radial Profiles Kapnulin, Lev Heimowitz, Ayelet Sharon, Nir Quantitative Methods Image and Video Processing The process of particle picking, a crucial step in cryo-electron microscopy (cryo-EM) image analysis, often encounters challenges due to outliers, leading to inaccuracies in downstream processing. In response to this challenge, this research introduces an additional automated step to reduce the number of outliers identified by the particle picker. The proposed method enhances both the accuracy and efficiency of particle picking, thereby reducing the overall running time and the necessity for expert intervention in the process. Experimental results demonstrate the effectiveness of the proposed approach in mitigating outlier inclusion and its potential to enhance cryo-EM data analysis pipelines significantly. This work contributes to the ongoing advancement of automated cryo-EM image processing methods, offering novel insights and solutions to challenges in structural biology research. |
| title | Outlier Removal in Cryo-EM via Radial Profiles |
| topic | Quantitative Methods Image and Video Processing |
| url | https://arxiv.org/abs/2409.05523 |