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Self-supervised deep learning encodes high-resolution features of protein subcellular localization

التفاصيل البيبلوغرافية
العنوان: Self-supervised deep learning encodes high-resolution features of protein subcellular localization
المؤلفون: Hirofumi Kobayashi, Keith C. Cheveralls, Manuel D. Leonetti, Loic A. Royer
المصدر: Nature Methods. 19:995-1003
بيانات النشر: Springer Science and Business Media LLC, 2022.
سنة النشر: 2022
مصطلحات موضوعية: Cell Biology, Molecular Biology, Biochemistry, Biotechnology
الوصف: Explaining the diversity and complexity of protein localization is essential to fully understand cellular architecture. Here we present cytoself, a deep-learning approach for fully self-supervised protein localization profiling and clustering. Cytoself leverages a self-supervised training scheme that does not require preexisting knowledge, categories or annotations. Training cytoself on images of 1,311 endogenously labeled proteins from the OpenCell database reveals a highly resolved protein localization atlas that recapitulates major scales of cellular organization, from coarse classes, such as nuclear and cytoplasmic, to the subtle localization signatures of individual protein complexes. We quantitatively validate cytoself’s ability to cluster proteins into organelles and protein complexes, showing that cytoself outperforms previous self-supervised approaches. Moreover, to better understand the inner workings of our model, we dissect the emergent features from which our clustering is derived, interpret them in the context of the fluorescence images, and analyze the performance contributions of each component of our approach.
تدمد: 1548-7105
1548-7091
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::2d9ea941a2f6a6fbf4dec51b9cf22816
https://doi.org/10.1038/s41592-022-01541-z
حقوق: OPEN
رقم الأكسشن: edsair.doi...........2d9ea941a2f6a6fbf4dec51b9cf22816
قاعدة البيانات: OpenAIRE
الوصف
تدمد:15487105
15487091