A generalizable computational framework for integrating heterogeneous biomarkers into interpretable scalar risk...

Area of Science:

• Biomedical Informatics
• Computational Biology
• Psychiatric Biomarkers

Background:

• Integrating diverse biomarker data for mental health assessment is a significant challenge in biomedical informatics.
• Existing methods often struggle to create unified, interpretable, and clinically relevant representations from heterogeneous sources.

Purpose of the Study:

• To develop a computational framework for creating unified scalar representations from multiple blood-based biomarkers.
• To construct a specific index, the Mental Disorder Risk Index (MDRI), to capture latent structures associated with mental health status.

Main Methods:

• Developed a framework for constructing unified scalar representations using linear and nonlinear formulations.
• Employed deterministic and meta-heuristic optimization strategies for parameter estimation.
• Validated model performance using metrics like the Matthews correlation coefficient.

Main Results:

• Demonstrated the creation of stable scalar representations within a normalized biomarker space.
• Confirmed the robustness and stability of the underlying latent biomarker structure across various optimization paths.
• Sensitivity analysis revealed heterogeneous biomarker contributions, indicating the capture of complex multivariate relationships.

Conclusions:

• Established the computational feasibility of creating unified biomarker-based indices.
• Provided a foundation for developing interpretable, scalable, and generalizable representations of complex biomedical data.
• The proposed framework offers a structured approach to integrating multi-biomarker data for mental health research.