Tensor factorization toward precision medicine.

Area of Science:

• Biomedical Data Science
• Computational Biology
• Precision Medicine

Background:

• Biomedical data is rapidly increasing in volume and complexity, surpassing traditional matrix-based analysis methods.
• Precision medicine requires advanced analytical tools to handle diverse and large-scale datasets.

Purpose of the Study:

• To analyze the application of tensor factorization in biomedical fields.
• To highlight the potential of tensor methods for summarizing complex biomedical data and updating medical knowledge.

Main Methods:

• Review of existing literature on tensor factorization in areas like genotyping and phenotyping.
• Conceptual analysis of tensor factorizations for dimensionality reduction and data summarization.

Main Results:

• Tensor factorizations provide a multi-modal extension to matrix methods, aiding in dimensionality reduction.
• Identified latent groups within data for summarizing features and instances effectively.

Conclusions:

• Tensor applications show promise as a tool for integrating continuous scientific and clinical evidence to update medical knowledge.
• Further experimental research is encouraged to address challenges in factorization design, temporality integration, and scalability.