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Related Experiment Video

Updated: Jan 10, 2026

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FatePredictor: Cell fate decision-making prediction with an ensemble deep learning model.

Jiantao Shen1, Nan Chen1, Bowen Niu1

  • 1School of Mathematics, South China University of Technology, Guangzhou 510640, China.

Innovation (Cambridge (Mass.))
|November 21, 2025
PubMed
Summary

FatePredictor accurately predicts cell fate bifurcation and dynamics using optimal transport and deep learning. This computational framework enhances understanding of complex biological systems and cellular trajectories from single-cell data.

Keywords:
FatePredictorcell fate bifurcationcritical pointdeep learningdynamic cell trajectory

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Cell differentiation involves critical transitions known as cell fate decision-making or bifurcation.
  • Understanding these bifurcations offers insights into fundamental biological mechanisms.
  • Conventional methods struggle to accurately predict these transitions and infer dynamics from single-cell RNA sequencing (scRNA-seq) data.

Purpose of the Study:

  • To develop a novel computational framework, FatePredictor, for predicting cell fate bifurcation.
  • To accurately infer cell fate dynamics from single-cell data.
  • To identify key genes and pathways involved in cellular processes.

Main Methods:

  • FatePredictor integrates bifurcation theory and optimal transport theory.
  • It employs dynamic unbalanced optimal transport to reconstruct cell trajectories.
  • An ensemble deep learning model predicts the dynamics of cell fate bifurcation.

Main Results:

  • FatePredictor accurately predicts bifurcations in simulated and real scRNA-seq data.
  • The framework outperforms existing methods in predicting complex biological system dynamics.
  • It successfully unveils intricate cellular trajectories and identifies key genes/pathways.

Conclusions:

  • FatePredictor is a powerful and user-friendly tool for analyzing cell fate dynamics.
  • It advances the prediction of critical transitions in biological systems.
  • The framework provides deeper insights into the mechanisms governing cellular processes.