Tensor Factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction.

Area of Science:

• Biomedical data science
• Machine learning applications
• Precision medicine

Background:

• Heart failure with preserved ejection fraction (HFpEF) is a complex syndrome with heterogeneous patient populations.
• Advances in data collection yield deep phenotypic and trans-omic data for HFpEF patients.
• Improved patient subtyping is crucial for understanding HFpEF pathophysiology and developing targeted therapies.

Purpose of the Study:

• To review the application of tensor factorization for integrating multi-modal data in biomedical research.
• To propose tensor factorization methods for deriving clinically relevant HFpEF subtypes.
• To address challenges in applying tensor factorization for precision medicine in HFpEF.

Main Methods:

• Review of existing literature on tensor factorization in genotyping and phenotyping.
• Analysis of tensor factorization's capability to integrate deep phenotypic and trans-omic data.
• Exploration of tensor factorization formulations for identifying HFpEF subtypes.

Main Results:

• Tensor factorization offers a powerful approach for integrating multi-modal data, including phenotypic and omic data.
• This method can reduce dimensionality and identify latent patient groups for better summarization.
• Potential for discovering HFpEF subtypes with distinct pathophysiologies and treatment responses.

Conclusions:

• Tensor factorization holds promise for advancing precision medicine in HFpEF by enabling data integration and subtype discovery.
• Further research is needed to address challenges such as incorporating prior medical knowledge and ensuring interpretability.
• Experimental studies are encouraged to validate and refine tensor factorization approaches for HFpEF patient stratification.