Big data for biomedical research: Current status and prospective

Abstract

At the frontier of cross-disciplinary sciences, biomedical research combines theory with methods, and biomedical sciences with computation. The recent in-depth integration of advanced equipment and information technology in biotechnology has led to an explosion of data collection, and thus there is a great need for data storage and analysis. Furthermore, the big data era is impacting greatly on biomedical research. In particular, research is transforming from hypothesis-driven to data-driven investigations. For decades, molecular biology research has been hypothesis driven, but the availability of massive biomedical data now allows researchers to directly explore the regularity contained in the data, make assumptions, and draw conclusions. With the fast accumulation of biomedical data, many problems that were unsolvable in the past can now be solved by carefully designed data analysis methods. At the same time, many new problems in biomedical research have emerged. Examples of big data technologies and applications include personalized genomics, transcriptomic and proteomic studies, genotyping and phenotyping of single cells, microbial community research, and biomedical imaging. All these applications are both data intensive and computation intensive, and thus advanced storage and analysis strategies characterized as being high throughput, high efficiency and high accuracy, are urgently needed to process these massive biological data. In this article, we summarize and review several aspects of biomedical big data (data generation, management, and analysis) and focus on data analysis and the application prospects of newly emerging data including human microbiota, the phenotype and genotype of single cells, and biomedical imaging. We conclude that biomedical big data is gaining momentum, although current hardware and software platforms for data-driven analysis remain a significant hurdle. We expect that as big data analysis breaks through this bottleneck, the in-depth research of biomedical big data will make a more significant contribution to clinical diagnosis and treatment.