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Related Experiment Video

Updated: Aug 9, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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A Score-Based Approach for Training Schrödinger Bridges for Data Modelling.

Ludwig Winkler1, Cesar Ojeda2,3, Manfred Opper2,3,4

  • 1Machine Learning Group, Technische Universität Berlin, 10623 Berlin, Germany.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

Schrödinger bridges, a method for generative modeling, were enhanced using a novel score-function approach. This technique efficiently estimates reverse drifts for time-reversed processes, improving generative data modeling and analyzing genetic data evolution.

Keywords:
Schrödinger bridge problemreverse-time stochastic processesscore estimation

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Area of Science:

  • Computational statistics
  • Machine learning
  • Bioinformatics

Background:

  • Schrödinger bridges are stochastic processes connecting probability distributions over time.
  • They are increasingly used for generative data modeling.
  • Training these models requires estimating the drift function of a time-reversed process.

Purpose of the Study:

  • To introduce an efficient method for computing reverse drifts in Schrödinger bridges.
  • To apply this method to artificial and real-world genetic data.

Main Methods:

  • A modified score-function-based method was developed.
  • This method is efficiently implemented using a feed-forward neural network.
  • The approach was tested on artificial datasets of varying complexity.

Main Results:

  • The modified score-function method enables efficient computation of reverse drifts.
  • The approach demonstrated effectiveness on artificial datasets.
  • Successful application to genetic data for modeling single-cell RNA measurements was achieved.

Conclusions:

  • The proposed method offers an efficient way to train Schrödinger bridges.
  • This advancement facilitates generative modeling and analysis of time-evolving data, including genetic sequences.