چکیده مقاله
The determination of stem cell fate is a fundamental process in developmental biology that governs tissue formation and homeostasis Traditionally, identifying the lineage commitment of a cell required invasive staining or destructive molecular analysis This paper explores the integration of Deep Neural Networks DNNs with non invasive time lapse imaging to predict cellular transitions before they become morphologically or molecularly evident By leveraging Convolutional Neural Networks CNNs and Recurrent Neural Networks RNNs , researchers can now extract subtle spatiotemporal features from live cell imaging The study highlights how these computational models achieve high accuracy in forecasting differentiation pathways, thereby revolutionizing regenerative medicine and developmental research
کلیدواژهها
نویسندگان
Neda Farhadi
Specialist
شیوه ارجاع
Farhadi, Neda,1404,Application of Deep Neural Networks in Predicting Stem Cell Fate Based on Time-Lapse Imaging Data,29th National Conference on Electrical, Computer and Mechanical Engineering,Shirvan
ارائهشده در
مجموعه مقالات بیست و نهمین کنفرانس ملی مهندسی برق ،کامپیوتر و مکانیک28 بهمن 1404 · شیروان