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Knowledge-Guided Gene Panel Selection for Label-Free Single-Cell RNA-Seq Data: A Reinforcement Learning Perspective
IEEE Transactions on Computational Biology and Bioinformatics
|September 15, 2025
Summary
This study introduces a novel gene panel selection method using ensemble knowledge and reinforcement learning (RL) to improve genomic biomarker discovery in label-free datasets. The approach enhances precision and efficiency, advancing single-cell genomics analysis.
Area of Science:
- Genomics
- Biomarker Discovery
- Machine Learning
Background:
- Gene panel selection is crucial for identifying genomic biomarkers in label-free data.
- Traditional methods often suffer from bias and inefficiency, hindering biological signal detection.
Purpose of the Study:
- To develop an improved iterative gene panel selection strategy.
- To mitigate biases and enhance efficiency in biomarker discovery using label-free genomic datasets.
Main Methods:
- Harnessing ensemble knowledge from existing gene selection algorithms for initial search space guidance.
- Integrating reinforcement learning (RL) with expert-shaped reward functions for dynamic refinement.
- Applying the method to label-free genomic datasets for biomarker identification.
Main Results:
- Demonstrated improved precision and efficiency in gene panel selection.
- Successfully identified informative genomic biomarkers.
- Validated through comparative experiments, case studies, and downstream analyses.
Conclusions:
- The proposed iterative strategy effectively addresses limitations of traditional gene panel selection methods.
- This approach holds significant potential for advancing single-cell genomics data analysis.
- Offers a more robust and adaptable solution for label-free biomarker discovery.
