[HTML][HTML] Big data analytics for personalized medicine
D Cirillo, A Valencia - Current opinion in biotechnology, 2019 - Elsevier
Current opinion in biotechnology, 2019•Elsevier
Highlights•Big Data are radically transforming Personalized Medicine.•Multi-omics, images,
device data, and electronic health records represent the main big data types in biomedical
research.•Cloud computing and HPC are the mainstream infrastructures for the
management and analysis of biomedical big data.•Multi-view data analysis requires
advanced machine learning techniques such as deep learning, and cognitive computing.Big
Data are radically changing biomedical research. The unprecedented advances in …
device data, and electronic health records represent the main big data types in biomedical
research.•Cloud computing and HPC are the mainstream infrastructures for the
management and analysis of biomedical big data.•Multi-view data analysis requires
advanced machine learning techniques such as deep learning, and cognitive computing.Big
Data are radically changing biomedical research. The unprecedented advances in …
Highlights
- Big Data are radically transforming Personalized Medicine.
- Multi-omics, images, device data, and electronic health records represent the main big data types in biomedical research.
- Cloud computing and HPC are the mainstream infrastructures for the management and analysis of biomedical big data.
- Multi-view data analysis requires advanced machine learning techniques such as deep learning, and cognitive computing.
Big Data are radically changing biomedical research. The unprecedented advances in automated collection of large-scale molecular and clinical data pose major challenges to data analysis and interpretation, calling for the development of new computational approaches. The creation of powerful systems for the effective use of biomedical Big Data in Personalized Medicine (aka Precision Medicine) will require significant scientific and technical developments, including infrastructure, engineering, project and financial management. We review here how the evolution of data-driven methods offers the possibility to address many of these problems, guiding the formulation of hypotheses on systems functioning and the generation of mechanistic models, and facilitating the design of clinical procedures in Personalized Medicine.
Elsevier