scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell Data

scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell Data

Single-cell RNA sequencing now profiles whole transcriptomes for hundreds of thousands of cells, yet existing trajectory-inference tools rarely pinpoint where and when fate decisions are made. We present single-cell reinforcement learning (scRL), an actor–critic framework that recasts differentiatio...

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Bibliographic Details
Main Authors: Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Biology
Subjects:
single–cell
reinforcement learning
actor–critic
fate decisions
trajectory inference
dimensionality reduction
Online Access:https://www.mdpi.com/2079-7737/14/6/679
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