LeMoNe

LeMoNe reconstructs regulatory module networks from gene expression data and integrates microRNA (miRNA) expression profiles and clinical parameters to identify co-regulated gene modules and their regulators for investigating disease mechanisms such as cancer.

Key Features:

• Integration of multi-omics data: Combines gene expression data with microRNA (miRNA) expression profiles and clinical parameters.
• Network reconstruction: Reconstructs regulatory module networks and identifies modules enriched for functional categories such as cell cycle-related genes.
• Identification of novel regulators: Predicts regulators that control module expression, highlighting both previously characterized and novel microRNAs (miRNAs) linked to disease.
• Clinical correlation: Correlates condition-dependent module expression with clinical parameters, including indicators of cancer aggressiveness.

Scientific Applications:

• Cancer research: Dissects gene-regulatory interactions relevant to cancer by reconstructing module networks.
• Prostate cancer studies: Analyzes lymphoblastoid cell lines derived from prostate cancer patients to distinguish aggressive and non-aggressive disease-associated modules.
• Biomarker and target discovery: Supports identification of candidate biomarkers and therapeutic targets through module and regulator analysis.
• Systems biology of complex diseases: Provides a framework for studying gene regulation in other complex diseases by integrating multiple data types.

Methodology:

Combines gene expression, miRNA profiles, and clinical parameters; reconstructs regulatory module networks to identify modules and enriched functional categories; and predicts module regulators, including miRNAs.