Cell cycle state prediction using graph neural networks

Mitosis is a crucial process ensuring the faithful transmission of the genetic information stored in the cell nucleus. Aberrations in this intricate process pose a significant threat to an organism's health, leading to conditions like cancer and various diseases. Hence, the study of mitosis holds paramount importance. Recent investigations have involved manual and semi-automated analyses of time-lapse microscopy images to understand mitosis better. This paper introduces an approach for predicting mitosis stages, employing a Convolutional Neural Network (CNN) as the initial feature extractor, followed by a Graph Neural Network (GNN) for predicting cell cycle states. A distinctive timestamp is incorporated into the feature vectors, treating this information as a graph to leverage internal interactions for predicting the subsequent cell state. To assess performance, experiments were conducted on three datasets, demonstrating that our method exhibits comparable efficacy to state-of-the-art techniques. ### Competing Interest Statement The authors have declared no competing interest.