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

By A. Alyass, Michelle Turcotte, D. Meyre. BMC Medical Genomics, 2015

471 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Health, Toxicology and Mutagenesis, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cae80eca52369e50037cc761abc0c3d3395881ba

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Citations in the corpus

Big Data and cloud computing: innovation opportunities and challenges (2017, background): “In summary, the Big Data arena ushers in great opportunities and changes in digital earth arena on how we live, think and work (Mayer-Schönberger and Cukier 2013), including personalized medicine (Alyass, Turcotte, and Meyre 2015), customized product recommendations and travel options.”
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “Adequate, analytical and intelligent access to healthcare data has the potential to revolutionize the field of medicine by improving the quality and transition of care, improving outcomes by reducing cost, detecting diseases at earlier stages (10,11) and developing a better understanding of biologic”
Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment (2019, unknown): “…marketing approaches and prioritize high-profit segments (Akter & Wamba, 2016; Fan et al., 2015), help develop customized products and services (Alyass, Turcotte, & Meyre, 2015), make more fine-grained and personalized recommendations for future purchases (Ngai, Gunasekaran, Wamba, Akter, &…”
Medical big data: promise and challenges (2017, background): “There is an overall tradeoff between a false positive rate and the benefit of identifying novel insights [25].”
Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma (2019, background): “However, unintended errors in annotations and sample mislabeling often occur when generating and managing large-scale data (Alyass et al., 2015).”