Informatics Approaches for Harmonized Intelligent Integration of Stem Cell Research.
Joseph FinkelsteinIrena ParvanovaFrederick ZhangPublished in: Stem cells and cloning : advances and applications (2020)
As biomedical data integration and analytics play an increasing role in the field of stem cell research, it becomes important to develop ways to standardize, aggregate, and share data among researchers. For this reason, many databases have been developed in recent years in an attempt to systematically warehouse data from different stem cell projects and experiments at the same time. However, these databases vary widely in their implementation and structure. The aim of this scoping review is to characterize the main features of available stem cell databases in order to identify specifications useful for implementation in future stem cell databases. We conducted a scoping review of peer-reviewed literature and online resources to identify and review available stem cell databases. To identify the relevant databases, we performed a PubMed search using relevant MeSH terms followed by a web search for databases which may not have an associated journal article. In total, we identified 16 databases to include in this review. The data elements reported in these databases represented a broad spectrum of parameters from basic socio-demographic variables to various cells characteristics, cell surface markers expression, and clinical trial results. Three broad sets of functional features that provide utility for future stem cell research and facilitate bioinformatics workflows were identified. These features consisted of the following: common data elements, data visualization and analysis tools, and biomedical ontologies for data integration. Stem cell bioinformatics is a quickly evolving field that generates a growing number of heterogeneous data sets. Further progress in the stem cell research may be greatly facilitated by development of applications for intelligent stem cell data aggregation, sharing and collaboration process.
Keyphrases
- stem cells
- big data
- electronic health record
- artificial intelligence
- machine learning
- clinical trial
- healthcare
- cell therapy
- systematic review
- primary care
- data analysis
- health information
- induced apoptosis
- quality improvement
- cell death
- deep learning
- cell surface
- double blind
- binding protein
- endoplasmic reticulum stress
- placebo controlled