Identifying Cancer genes by combining two-rounds RWR based on multiple biological data

Area of Science:

• Bioinformatics
• Genomics
• Molecular Biology

Background:

• Identifying cancer genes is vital for understanding biomolecular mechanisms and advancing bioinformatics.
• Existing methods often use limited biological data, neglecting multifaceted gene-disease relationships.

Purpose of the Study:

• To propose a novel algorithm for identifying cancer genes.
• To leverage multiple biological data sources for a comprehensive analysis.

Main Methods:

• A two-round random walk algorithm (TRWR-MB) was developed.
• The algorithm integrates protein-protein interaction (PPI) networks, pathway networks, microRNA and lncRNA similarity networks, cancer similarity networks, and protein complexes.
• Two random walk phases utilize different seed nodes and heterogeneous networks to identify potential and specific cancer genes.

Main Results:

• The TRWR-MB algorithm achieved a high area under the receiver operating characteristic curve (AUC).
• Case studies demonstrated the algorithm's effectiveness in identifying novel cancer genes.

Conclusions:

• TRWR-MB effectively integrates diverse biological data to identify cancer genes.
• The approach provides a multi-perspective analysis of gene-cancer relationships.