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Precision medicine's inevitable trajectory toward rare-disease-sized cohorts: implications for machine learning and
Andrew Janowczyk1, Doron Merkler2, Olivier Michielin3
1Department of Biomedical Engineering, Emory University, Atlanta, GA, USA; Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA; Department of Oncology, Division of Precision Oncology, University Hospitals of Geneva, Geneva, Switzerland; Department of Diagnostics, Division of Clinical Pathology, University Hospitals of Geneva, Geneva, Switzerland.
Precision medicine faces challenges with rare-disease-sized cohorts (RDSCs) due to small, fragmented datasets. New methods and systemic changes in data infrastructure are crucial for advancing personalized care.
Area of Science:
- Biomedical Informatics
- Genomics
- Data Science
Background:
- Precision medicine tailors treatments to specific patient subgroups.
- The rise of rare-disease-sized cohorts (RDSCs) poses challenges for traditional data analysis.
- RDSCs are small, fragmented datasets arising from personalized care approaches.
Purpose of the Study:
- To explore the tension between personalized care and data limitations.
- To highlight the need for novel methods for small, high-dimensional, heterogeneous datasets.
- To propose a roadmap for advancing precision medicine.
Main Methods:
- Literature review and synthesis of challenges in precision medicine data.
- Analysis of limitations in current deep learning and machine learning approaches.
- Drawing lessons from rare disease research methodologies.
Main Results:
- Traditional machine learning struggles with RDSCs.
- Technical innovation alone is insufficient.
- Systemic changes in biobanking, data standardization, and collaboration are vital.
Conclusions:
- Advancing precision medicine requires new analytical methods for small datasets.
- Investment in data collection, usage, and governance is essential.
- A collaborative approach is needed to realize the potential of personalized care.
