MOAI

MOAI identifies interactions between risk factors that jointly influence multiple outcomes in multifactorial disease studies.

Key Features:

• Simultaneous Interaction Estimation: Estimates interactions occurring simultaneously across multiple outcomes.
• Pareto Set Filter Operator Utilization: Leverages the Pareto set filter operator to detect multi-outcome interactions.
• Applicability in Population-Based Studies: Applicable to population-based study designs for epidemiological analyses.

Scientific Applications:

• Performance benchmarking: Demonstrated performance compared to existing approaches in identifying multi-outcome interactions.
• Colorectal cancer (CRC) prognostic analysis: Used to explore interactions between risk factors affecting prognostic outcomes such as metastases and mortality in CRC.
• Vaspin–CEA interaction discovery: Identified an interaction in CRC where vaspin ≥30% and carcinoembryonic antigen (CEA) ≥5 associate with increased risks of metastases and mortality.
• Framework for multifactorial diseases: Provides a framework for investigating other multifactorial conditions where shared risk factors influence multiple outcomes.

Methodology:

MOAI integrates the Pareto set filter operator to isolate and analyze interactions between multiple outcomes.