Publications by authors named "Anastasia Razdaibiedina"
We previously constructed TheCellVision.org, a central repository for visualizing and mining data from yeast high-content imaging projects. At its inception, TheCellVision.
View Article and Find Full Text PDFFluorescence microscopy data describe protein localization patterns at single-cell resolution and have the potential to reveal whole-proteome functional information with remarkable precision. Yet, extracting biologically meaningful representations from cell micrographs remains a major challenge. Existing approaches often fail to learn robust and noise-invariant features or rely on supervised labels for accurate annotations.
View Article and Find Full Text PDFArticle Synopsis
- Fluorescence microscopy provides insights into protein localization at a single-cell level, but extracting meaningful biological information from these images is challenging due to noise and reliance on labeled data.
- The researchers developed a self-supervised method called PIFiA for protein functional annotation from single-cell imaging, which outperforms existing methods by generating detailed protein feature profiles.
- PIFiA enables various analyses, such as clustering protein functions, studying cell population differences, and identifying multi-localization patterns, while a colocalization assay validates its predictions and reveals new biological functions, along with an interactive website for user access.