ComHub

ComHub predicts hub transcription factors in gene regulatory networks from gene expression data to identify key regulatory nodes relevant for disease regulation and drug targeting.

Key Features:

• Hub prediction from gene expression: Predicts hub transcription factors that regulate numerous target genes using gene expression–derived network information.
• Community prediction approach: Combines a compendium of network inference methods to inform hub detection.
• Consensus by averaging: Generates consensus hub rankings by averaging predictions from multiple network inference algorithms.
• Versatile network input: Accepts predefined networks and can perform standard network inference from expression data.
• Benchmarking and validation: Validated using DREAM5 challenge data and an independent human gene expression dataset.

Scientific Applications:

• Disease research: Identification of hub transcription factors to elucidate regulatory mechanisms implicated in disease.
• Drug discovery: Prioritization of key regulatory nodes within GRNs to inform potential therapeutic target selection.

Methodology:

Integrates multiple network inference methods and produces consensus hub predictions by averaging algorithmic outputs; operates on predefined networks or by inferring networks from gene expression data.