WMDS.net

WMDS.net identifies key regulators in transcriptome programs by applying network controllability and structural controllability theory to differential gene co-expression networks.

Key Features:

• Network controllability framework: Applies network controllability and structural controllability theory to pinpoint driver nodes that can guide transcriptional programs.
• Minimum Dominating Set (MDS) identification: Determines a minimum dominating set of driver nodes within differential gene co-expression networks to locate regulators controlling state transitions.
• Weighted MDS network model: Implements a weighted MDS model that integrates node degree and the significance of gene co-expression differences between physiological states to quantify node controllability.
• Application to cancer RNA-seq (TCGA): Has been applied to identify cancer driver genes using RNA-seq datasets from The Cancer Genome Atlas (TCGA), demonstrating a favorable balance between precision and recall across cancer datasets.

Scientific Applications:

• Transcriptome network analysis: Identification of key regulatory nodes in differential gene co-expression networks for transcriptomic studies.
• Cellular state transitions and reprogramming: Analysis of driver nodes that may control transitions between cellular phenotypes and reprogramming processes.
• Cancer driver gene discovery: Prioritization of candidate cancer driver genes from RNA-seq data, supporting studies of disease progression and targeted interventions.

Methodology:

Applies network controllability and structural controllability theory; computes a minimum dominating set of driver nodes on differential gene co-expression networks; uses a weighted MDS model combining node degree and significance of gene co-expression differences; applied to RNA-seq datasets from TCGA.