Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

Area of Science:

• Biomedical Big Data Analytics
• Precision Medicine
• Computational Biology

Background:

• Explosive growth in biomedical big data presents opportunities and challenges for precision medicine.
• Automated collection of large-scale molecular and clinical data necessitates novel computational approaches.
• Key challenges include data dimensionality, heterogeneity, missing values, class imbalance, and scalability.

Purpose of the Study:

• To review recent progress and breakthroughs in applying big data within precision medicine.
• To summarize key aspects including content, data sources, technologies, tools, challenges, and gaps.
• To discuss nine critical fields impacting big data in precision medicine.

Main Methods:

• Literature review and synthesis of recent advancements in biomedical big data.
• Categorization of big data applications across nine key fields.
• Analysis of challenges and existing gaps in big data management and interpretation.

Main Results:

• Significant progress has been made in leveraging big data for precision medicine.
• Nine fields, including data warehousing, EMRs, imaging, AI in surgery, omics, health monitoring, knowledge graphs, public health, and security, are crucial.
• Novel computational approaches are essential to address data analysis and interpretation challenges.

Conclusions:

• Efficient management and analysis of biomedical big data are vital for advancing precision medicine.
• Addressing challenges like data heterogeneity and scalability is key to unlocking big data's potential.
• Continued development of computational tools and strategies is necessary for translational applications.