دورية أكاديمية
Missing Wedge Completion via Unsupervised Learning with Coordinate Networks
| العنوان: | Missing Wedge Completion via Unsupervised Learning with Coordinate Networks |
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| المؤلفون: | Dave Van Veen, Jesús G. Galaz-Montoya, Liyue Shen, Philip Baldwin, Akshay S. Chaudhari, Dmitry Lyumkis, Michael F. Schmid, Wah Chiu, John Pauly |
| المصدر: | International Journal of Molecular Sciences, Vol 25, Iss 10, p 5473 (2024) |
| بيانات النشر: | MDPI AG, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Biology (General) LCC:Chemistry |
| مصطلحات موضوعية: | machine learning, artificial intelligence, coordinate networks, unsupervised learning, missing wedge, cryogenic electron tomography (cryoET), Biology (General), QH301-705.5, Chemistry, QD1-999 |
| الوصف: | Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, which limits reconstruction quality due to incomplete data collection angles. Recently, supervised deep learning methods leveraging convolutional neural networks (CNNs) have considerably addressed this issue; however, their pretraining requirements render them susceptible to inaccuracies and artifacts, particularly when representative training data is scarce. To overcome these limitations, we introduce a proof-of-concept unsupervised learning approach using coordinate networks (CNs) that optimizes network weights directly against input projections. This eliminates the need for pretraining, reducing reconstruction runtime by 3–20× compared to supervised methods. Our in silico results show improved shape completion and reduction of missing wedge artifacts, assessed through several voxel-based image quality metrics in real space and a novel directional Fourier Shell Correlation (FSC) metric. Our study illuminates benefits and considerations of both supervised and unsupervised approaches, guiding the development of improved reconstruction strategies. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1422-0067 1661-6596 |
| Relation: | https://www.mdpi.com/1422-0067/25/10/5473; https://doaj.org/toc/1661-6596; https://doaj.org/toc/1422-0067 |
| DOI: | 10.3390/ijms25105473 |
| URL الوصول: | https://doaj.org/article/78a7161da6ad462ca9f52f562d8b4c34 |
| رقم الأكسشن: | edsdoj.78a7161da6ad462ca9f52f562d8b4c34 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14220067 16616596 |
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| DOI: | 10.3390/ijms25105473 |