Phantom

Phantom applies multi-objective optimization to detect heterogeneous, time-dependent changes within subsets of genes in time-course gene expression data.

Key Features:

• Multi-Objective Optimization: Simultaneously evaluates multiple objectives to capture complex patterns of heterogeneity within gene sets.
• Temporal Dependency Modeling: Incorporates temporal dependencies of time-course data to characterize dynamic changes in gene subsets over time.
• Enhanced Detection Performance: Improves detection of biological changes across time points relative to single-objective approaches.

Scientific Applications:

• Gene Expression Studies: Dissects how specific gene subsets exhibit distinct temporal responses in time-course expression experiments.
• Disease Progression Analysis: Identifies heterogeneous temporal expression patterns relevant to disease progression and treatment response.
• Biological Module Investigation: Reveals differential temporal behaviors within subcomponents of pathways or biological modules.

Methodology:

Uses a statistical framework that leverages multi-objective optimization and models temporal dependencies to assess heterogeneity within gene sets.