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Navigating the Multiverse: a Hitchhiker's guide to selecting harmonization methods for multimodal biomedical data.

Murali Aadhitya Magateshvaren Saras^1,2,3, Mithun K Mitra², Sonika Tyagi^3,4

¹IITB-Monash Research Academy, Mumbai, Maharashtra 400076, India.

Biology Methods & Protocols

|May 1, 2025

Summary

This summary is machine-generated.

This study introduces a comprehensive taxonomy and guide for multimodal data analysis using machine learning (ML). It details methods for data representation and integration, aiding researchers in advancing personalized medicine.

Keywords:

data integration deep learning digital health feature representation multimodal integration

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Area of Science:

Biomedical Informatics
Computational Biology
Machine Learning

Background:

Machine learning (ML) significantly enhances biological system comprehension through predictive modeling.
Multimodal data analysis, integrating diverse data types, shows superior performance over single-modality approaches.
A lack of comprehensive taxonomy for ML multimodal architectures hinders effective application.

Purpose of the Study:

To develop a robust framework and taxonomy for multimodal ML analysis.
To categorize ML architectures for multimodal data, detailing pros and cons.
To provide a practical guide for selecting and implementing multimodal analysis workflows.

Main Methods:

Harmonization of multimodal data is presented as a dual process: representation and integration.
A taxonomy classifies various representation and integration methods into six categories.
A 10-step guide flowchart is provided for implementing multimodal workflows.

Main Results:

The study offers a detailed taxonomy of multimodal data harmonization methods.
Advantages and disadvantages of each method are elucidated.
A practical, step-by-step guide facilitates the adoption of multimodal approaches.

Conclusions:

This work provides essential guidance for navigating complex biomedical and clinical data analysis.
The developed framework supports informed decision-making in multimodal data integration.
It is a crucial step towards advancing personalized medicine through advanced data analytics.