MNI

MNI identifies gene targets and molecular mediators of drug treatments, diseases, and physiological states from gene-expression data using network-based statistical modeling.

Key Features:

• Gene-target identification: Identifies gene targets and molecular mediators associated with drug treatments, diseases, or physiological states from gene-expression profiles.
• Statistical modeling: Constructs a statistical model from a training dataset comprising hundreds of expression profiles that encodes combinatorial interactions among genes to represent gene-regulatory networks.
• Expression profile filtering: Applies the constructed model to experimental treatment expression profiles to filter and isolate molecular mediators responsible for observed responses from other differentially expressed genes.
• Efficiency and scalability: Typical runtime is approximately one hour per run, with runtime varying according to genome size and dataset scale.

Scientific Applications:

• Drug development: Identifies specific gene targets affected by therapeutic compounds to inform mechanisms of action.
• Disease mechanism elucidation: Identifies gene mediators associated with diseases to support understanding of pathogenesis and progression.
• Physiological state analysis: Analyzes genetic regulation underlying different physiological states in cells and tissues.

Methodology:

Collect a comprehensive training set of gene-expression profiles; construct a statistical model representing gene interactions and gene-regulatory networks from the training data; analyze experimental treatment expression profiles with the constructed model to filter and identify key molecular targets or mediators.