Multi-hierarchical profiling: an emerging and quantitative approach to characterizing diverse biological networks

Area of Science:

• Systems biology
• Bioinformatics
• Structural biology

Background:

• Biological networks exhibit multi-hierarchical structures crucial for cellular development, disease progression, and variation.
• Recent advancements in omics data analysis, structural biology, and bioinformatics have accelerated research in network hierarchies.

Purpose of the Study:

• To discuss and compare techniques for quantifying multilevel hierarchies in biological networks.
• To identify the features, capabilities, and drawbacks of these methods across various applications.
• To address challenges in functional hierarchical identification and provide analytical suggestions.

Main Methods:

• Classification of quantification methods into three types: topological spatial-scales, multilevel hierarchical control, and feature ordering.
• Comparative analysis of existing techniques based on their strengths and weaknesses.
• Review of methods for analyzing dynamic data in complex network studies.

Main Results:

• Several techniques exist for quantifying multilevel hierarchies, each with specific applications and limitations.
• Functional hierarchical identification presents notable challenges.
• The study categorizes methods to aid researchers in selecting appropriate approaches.

Conclusions:

• Multi-hierarchical profiling is essential for understanding biological network dynamics in health and disease.
• Further development is needed to overcome limitations in functional hierarchy identification.
• The proposed classification and analytical suggestions can guide future research in complex network analysis.