A Comprehensive Survey of Tools and Software for Active Subnetwork Identification

Area of Science:

• Computational biology
• Bioinformatics
• Systems biology

Background:

• Integrating molecular profiles and network databases is crucial for understanding dynamic biological mechanisms and disease states.
• Identifying condition-specific active modules reveals underlying phenomena and disease progression.

Purpose of the Study:

• To review and compare 22 integrative approaches for active module identification.
• To guide researchers in selecting appropriate computational tools based on their data and analysis goals.

Main Methods:

• Systematic review of 22 active module identification tools published in the last decade.
• Comparison based on features, data integration capabilities, network structures, mathematical models, and implementation.
• Focus on currently available and well-maintained software.

Main Results:

• Detailed comparison of 22 integrative methods for active module identification.
• Highlighting successful real-world applications of these methods.
• Identification of current challenges and limitations in the field.

Conclusions:

• Provides a comprehensive overview to aid researchers in choosing suitable active module identification tools.
• Emphasizes the importance of selecting methods aligned with specific research questions and data types.
• Identifies areas for future development in integrative computational biology approaches.