Prediction of Cellular Identities from Trajectory and Cell Fate Information

arXiv:2401.06182v2 Announce Type: replace-cross
Abstract: Determining cell identities in imaging sequences is an important yet challenging task. The conventional method for cell identification is via cell tracking, which is complex and can be time-consuming. In this study, we propose an innovative approach to cell identification during early $\textit{C. elegans}$ embryogenesis using machine learning. Cell identification during $\textit{C. elegans}$ embryogenesis would provide insights into neural development with implications for higher organisms including humans. We employed random forest, MLP, and LSTM models, …

abstract arxiv cellular cs.cv cs.lg eess.iv identification imaging information prediction q-bio.qm study tracking trajectory type via