Topological data analysis in biomedicine: A review

• Published in: Journal of biomedical informatics Vol. 130; p. 104082
• Main Authors: Skaf, Yara, Laubenbacher, Reinhard
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.06.2022
• Subjects: Applied topology; Big data analytics; Biomedical informatics; Data Analysis; Diagnostic Imaging; Machine learning; Mapper; Persistent homology; Personalized medicine; TDA; Topological data analysis; TDA; Mapper; Persistent homology; Biomedical informatics; Applied topology; Machine learning; Big data analytics; Personalized medicine; Topological data analysis
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2022.104082

[Display omitted] •Analyzing “big data” can be extremely challenging, especially in biomedical fields.•TDA approaches may uncover insights from data missed by traditional analysis.•TDA converts large, complex data sets into simple summaries of their key features.•Applications of TDA include precision medicine, structural biology, cell phenotyping. Significant technological advances made in recent years have shepherded a dramatic increase in utilization of digital technologies for biomedicine– everything from the widespread use of electronic health records to improved medical imaging capabilities and the rising ubiquity of genomic sequencing contribute to a “digitization” of biomedical research and clinical care. With this shift toward computerized tools comes a dramatic increase in the amount of available data, and current tools for data analysis capable of extracting meaningful knowledge from this wealth of information have yet to catch up. This article seeks to provide an overview of emerging mathematical methods with the potential to improve the abilities of clinicians and researchers to analyze biomedical data, but may be hindered from doing so by a lack of conceptual accessibility and awareness in the life sciences research community. In particular, we focus on topological data analysis (TDA), a set of methods grounded in the mathematical field of algebraic topology that seeks to describe and harness features related to the “shape” of data. We aim to make such techniques more approachable to non-mathematicians by providing a conceptual discussion of their theoretical foundations followed by a survey of their published applications to scientific research. Finally, we discuss the limitations of these methods and suggest potential avenues for future work integrating mathematical tools into clinical care and biomedical informatics.