Abstract
Biomedical platforms provide the hardware and software to securely ingest, process, validate, curate, store, and share data. Many large-scale biomedical platforms use secure cloud computing technology for analyzing, integrating, and storing phenotypic, clinical, and genomic data. Several web-based platforms are available for researchers to access services and tools for biomedical research. The use of bio-containers can facilitate the integration of bioinformatics software with various data analysis pipelines. Adoption of Common Data Models, Common Data Elements, and Ontologies can increase the likelihood of data reuse. Managing biomedical Big Data will require the development of strategies that can efficiently leverage public cloud computing resources. The use of the research community developed standards for data collection can foster the development of machine learning methods for data processing and analysis. Increasingly platforms will need to support the integration of data from multiple disease area research.
Highlights
Biological data arises from a variety of sources – genomic sequencing, imaging studies, clinical, phenotypic, ecological, and microscopic research work
The Biomedical Research Informatics Computing System (BRICS) platform supports the use of Common Data Elements (CDEs) methodology during data collection, it utilizes data dictionaries that are based on CDEs for specific disease areas, examples in Table 1 are the TBI and PDBP platforms (Navale et al 2019)
Biomedical platforms are required for the collection, processing, analysis, storage, and access to research data
Summary
Biological data arises from a variety of sources – genomic sequencing, imaging studies, clinical, phenotypic, ecological, and microscopic research work. Managing the research data life cycle requires biomedical platforms to support comprehensive data management plans (Griffin et al 2017). Biomedical data platforms can provide scalable infrastructures (hardware and software), secure services to ingest, process, validate, curate, store, and share data. These platforms support workflow(s), data analyses, visualization tools, and access to storage repositories. Research communities need general-purpose biological, clinical, translational, and disease area research platforms. Several national and international platforms have been developed to support biological research. This article serves as an overview of platform capabilities, and the examples provided illustrate the diverse data content, infrastructure, services, tools, and methods for increasing access and use of biomedical research data
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