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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
HNPP: Higher-order network-based personalized PageRank for detecting critical phase in complex biological systems
Jiayuan Zhong1, Xuerong Gu2, Dandan Ding3
1School of Mathematics, Foshan University, Foshan, China.
Plos Computational Biology
|July 17, 2026
Summary
This study introduces a novel higher-order network-based personalized PageRank (HNPP) method to detect critical transitions in biological systems. HNPP enhances accuracy in single-cell analyses by capturing complex interactions beyond pairwise relationships.
Area of Science:
- Systems biology
- Computational biology
- Genomics
Background:
- Dynamic biological processes can undergo critical transitions between stable states.
- Identifying these critical states and signaling molecules is crucial for understanding biological mechanisms and enabling interventions.
- Current methods often rely on pairwise interactions, limiting their accuracy with high-dimensional, complex biological data like single-cell transcriptomics.
Purpose of the Study:
- To develop a robust framework for identifying critical phases and signaling molecules in biological systems at the single-cell level.
- To overcome the limitations of existing methods by incorporating higher-order interactions.
Main Methods:
- Proposed a novel framework: higher-order network-based personalized PageRank (HNPP).
- HNPP incorporates higher-order collaborative structures to capture many-body interaction patterns.
- Validated the framework using simulated and six real-world single-cell transcriptomic datasets.
Main Results:
- HNPP accurately characterizes and quantifies the criticality of complex biological systems.
- Demonstrated enhanced early-warning capabilities and higher accuracy compared to existing critical point detection methods.
- Identified signaling molecules were further validated through functional analysis.
Conclusions:
- The higher-order network-based personalized PageRank (HNPP) framework provides a more accurate and robust approach for critical point detection in single-cell transcriptomic data.
- HNPP offers improved early-warning signals for biological transitions.
- This method advances the understanding of complex biological processes and facilitates timely interventions.
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