FateNet: an integration of dynamical systems and deep learning for cell fate prediction.
Journal:
Bioinformatics (Oxford, England)
PMID:
39177093
Abstract
MOTIVATION: Understanding cellular decision-making, particularly its timing and impact on the biological system such as tissue health and function, is a fundamental challenge in biology and medicine. Existing methods for inferring fate decisions and cellular state dynamics from single-cell RNA sequencing data lack precision regarding decision points and broader tissue implications. Addressing this gap, we present FateNet, a computational approach integrating dynamical systems theory and deep learning to probe the cell decision-making process using scRNA-seq data.