ETNA

ETNA performs network analysis of protein-protein interaction networks (PPINs) to detect topological biases and support multi-omics interpretation for drug target identification and therapy outcome prediction.

Key Features:

• Topological analysis: Performs detailed topological analyses of PPINs, including datasets from the IntAct Molecular Database, with application to diseases such as cancer and Parkinson's disease.
• Bias detection methods: Implements topological methods to detect incompleteness, error-prone entries, and research-trend biases in protein-protein interaction data.
• Multi-omics integration: Models PPINs as networks to integrate and interpret multi-omics data within molecular pathways.
• Classical and simulation-based measures: Integrates classical network measures and simulation-based metrics to examine network structure.
• Target discovery and therapy prediction: Uses network modeling to identify potential drug targets and to predict therapy outcomes.

Scientific Applications:

• Drug target identification: Prioritizes candidate proteins as potential drug targets from PPIN topology.
• Therapy outcome prediction: Predicts therapy outcomes based on network characteristics and modeled interactions.
• Disease mechanism analysis: Refines structural interpretation of PPINs to study mechanisms in diseases including cancer and Parkinson's disease.
• Multi-omics interpretation: Supports integration of proteomics and other omics layers through network-based analyses.

Methodology:

ETNA applies topological analysis and topological bias-detection methods to PPINs modeled as networks and integrates classical and simulation-based network measures.