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On fusion methods for knowledge discovery from multi-omics datasets.

Edwin Baldwin¹, Jiali Han², Wenting Luo¹

¹Department of Biosystems Engineering, University of Arizona, United States.

Computational and Structural Biotechnology Journal

|March 25, 2020

Summary

This summary is machine-generated.

Multi-omics data analysis integrates diverse biological measurements for a holistic view. This study categorizes fusion methods (data, model, mixed) to address complex biological questions, offering insights for future research.

Keywords:

Data fusion Data integration Model fusion Multi-omics

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Area of Science:

Systems Biology and Bioinformatics
Genomics and Multi-omics Integration

Background:

Biological samples are increasingly measured across multiple omics scales for comprehensive understanding.
Understanding perturbations from genetic variants, environments, and their interactions requires integrated data analysis.
Multi-omics data presents significant challenges in data integration and fusion.

Purpose of the Study:

To review and categorize fusion-based analysis methods for multi-omics data.
To provide methodological and mathematical perspectives on data fusion techniques.
To exemplify characteristics, principles, and applications of different fusion approaches.

Main Methods:

Categorization of fusion methods into three types: data fusion, model fusion, and mixed fusion.
Methodological and mathematical examination of fusion approaches.
Illustrative examples for each fusion category.

Main Results:

Fusion-based analysis has advanced significantly due to application drivers and theoretical breakthroughs.
Detailed explanation of data fusion, model fusion, and mixed fusion methodologies.
Demonstration of typical examples for each fusion type.

Conclusions:

Fusion analysis is crucial for solving biological problems targeted by multi-omics measurements.
Discussion of current gaps and potential future research directions in multi-omics data fusion.
Highlights the importance of scalable and uniform methods for effective data fusion.