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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Biology |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-7737/14/6/679 |
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