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

Alyass A, Turcotte M, Meyre D - BMC Med Genomics (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, Hamilton, ON, Canada. alyassa@math.mcmaster.ca.

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.

No MeSH data available.


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
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Fig6: 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

Mentions: This review aims to stimulate research initiatives in the field of big data analysis and integration. Omics data embody a large mixture of signals and errors, where our current ability to identify novel associations comes at the cost of tolerating larger error thresholds in the context of big data. Major investments need to be made in the fields of bioinformatics, biomathematics, and biostatistics to develop translational analyses of omics data and make the best use of high-throughput technologies. New generations of multi-talented scientists and multidisciplinary research teams are required to build accurate complex disease models and permit effective personalized prevention, diagnosis and treatment strategies. Our ability to integrate and interoperate omics data is an important limiting factor in the transition to personalized medicine. Overcoming these limitations may boost the nation-wide implementation of omics facilities in clinical settings (Fig. 6). The subsequent economies of scale may in turn favor the access to personalized medicine to disadvantaged nations, repelling the growing shadow of two-tiered personalized medicine.Fig. 6


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

Alyass A, Turcotte M, Meyre D - BMC Med Genomics (2015)

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
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4482045&req=5

Fig6: 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
Mentions: This review aims to stimulate research initiatives in the field of big data analysis and integration. Omics data embody a large mixture of signals and errors, where our current ability to identify novel associations comes at the cost of tolerating larger error thresholds in the context of big data. Major investments need to be made in the fields of bioinformatics, biomathematics, and biostatistics to develop translational analyses of omics data and make the best use of high-throughput technologies. New generations of multi-talented scientists and multidisciplinary research teams are required to build accurate complex disease models and permit effective personalized prevention, diagnosis and treatment strategies. Our ability to integrate and interoperate omics data is an important limiting factor in the transition to personalized medicine. Overcoming these limitations may boost the nation-wide implementation of omics facilities in clinical settings (Fig. 6). The subsequent economies of scale may in turn favor the access to personalized medicine to disadvantaged nations, repelling the growing shadow of two-tiered personalized medicine.Fig. 6

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, Hamilton, ON, Canada. alyassa@math.mcmaster.ca.

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.

No MeSH data available.