Topological data analysis in biomedicine: A review

Area of Science:

• Biomedical Informatics
• Computational Biology
• Data Science

Background:

• Digital technologies are increasingly used in biomedicine, leading to vast amounts of data.
• Current data analysis tools struggle to extract meaningful knowledge from this digitized biomedical information.
• There is a need for advanced analytical methods to keep pace with biomedical data growth.

Purpose of the Study:

• To provide an overview of emerging mathematical methods for biomedical data analysis.
• To introduce topological data analysis (TDA) as a powerful technique for understanding data shape.
• To bridge the gap between advanced mathematical concepts and life sciences researchers.

Main Methods:

• Focus on topological data analysis (TDA), a field rooted in algebraic topology.
• Explain the theoretical foundations of TDA conceptually for non-mathematicians.
• Survey published applications of TDA in scientific research.

Main Results:

• TDA offers novel ways to analyze complex biomedical datasets by focusing on data topology.
• The article aims to increase awareness and accessibility of TDA within the life sciences.
• Demonstrates potential for TDA to enhance data interpretation in biomedical research.

Conclusions:

• TDA presents a promising approach to overcome limitations in current biomedical data analysis.
• Increased accessibility and understanding of TDA can significantly benefit researchers and clinicians.
• Future work should focus on integrating TDA and other mathematical tools into clinical practice and informatics.