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Proteins regulate biological functions through the formation of distinct protein complexes. Identification and characterization of these protein-protein interactions are critical to deciphering their mechanism of action. Different antibody-based or cross-linking-based methods have been developed to identify the protein-protein interactions.

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In mammalian oocytes, large-scale chromatin organization regulates transcription, nuclear architecture, and maintenance of chromosome stability in preparation for meiosis onset. Pre-ovulatory oocytes with distinct chromatin configurations exhibit profound differences in metabolic and transcriptional profiles that ultimately determine meiotic competence and developmental potential. Here, we developed a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live mouse oocytes.

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