From big data analysis to personalized medicine for all: challenges and opportunities

Abstract

Recent advances in high-throughput technologies have led to the emergence of systems biology as a holistic science to achieve more precise modeling of complex diseases. Many predict the emergence of personalized medicine in the near future. We are, however, moving from two-tiered health systems to a two-tiered personalized medicine. Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries. This is mirrored by an increasing lag between our ability to generate and analyze big data. Several bottlenecks slow-down the transition from conventional to personalized medicine: generation of cost-effective high-throughput data; hybrid education and multidisciplinary teams; data storage and processing; data integration and interpretation; and individual and global economic relevance. This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine.

Fig. 1

Distributions of populations and global health expenditure according to WHO 2012

Fig. 2

A basic framework of personalized medicine. The integration of omics profiles permit accurate modeling of complex diseases and opens windows of opportunities for innovative clinical applications to subsequently benefit the patient

Fig. 3

An interdisciplinary cloud-based model to implement personalized medicine. The consecutive knowledge and service swapping between modeling and software experts in research and development units is essential for the management, integration, and analysis of omics data. Thorough software and model development will derive updates upon knowledge bases for complex diseases, in addition to clinical utilities, commercial applications, privacy and access control, user-friendly interfaces, and advanced software for fast computations within the cloud. This translates into personalized medicine via personal clouds that upload wellness indices into personal devices, electronic databases for health professionals, and innovative medical devices

Fig. 4

The bias-variance tradeoff with increasing model complexity

Fig. 5

Noise and true heterogeneity within complex systems. Source of noise include measurement error and sampling variability. True heterogeneity however, is the result of true differences of effect sizes due to 1) the dynamic biological nature which encompasses feedback loops and temporal associations; and 2) multi-factorial complexity. Increasing the sample sizes is one solution to bypass noise and attain precise effect sizes, but true heterogeneity can only be adjusted during analysis when possible and via standardizations and calibrations that limit generalizability of the conclusions

Fig. 6

Bottleneck toward personalized medicine. The collective challenges to make the transition from conventional to personalized medicine include: i) generation of cost-effective high-throughput data; ii) hybrid education and multidisciplinary teams; iii) data storage and processing; iv) data integration and interpretation; and v) individual and global economic relevance. Massive global investment in basic research may precede global investment in public health for transformative medicine