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

Abstract

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

Keywords: biomedical big data; electronic medical record; federated learning; knowledge graph; medical imaging analysis; omics data; precision medicine.

Figure 1

Medical Image Analysis Routines.

Figure 2

Summary of omics data, available computational tools, and downstream analyses based on omics data.

Figure 3

Summary of Signatures: Sources, types, and applications. Biomedical big data can be collected from public databases and EHR. Signatures can be extracted from biomedical big data by using computational tools. These signatures can be used on multiple aspects such as diagnosis, therapy strategies and drug discovery. Red stars mean the structured signatures. HER, Electronic health record; csNMF, systematic compound signature discovery pipeline; EGEM, Enrichment of gene effects of a molecule; SPN, Segment polarity network; KIEP, Kinase inhibitor effect prediction.