Year: 2015
Type: editorial
Abstract: As we have officially entered the big data era, we are embracing numerous opportunities but also meeting strong demand on innovative approaches to managing and analyzing the data in the digital world. The speed of data generation is astonishing—from now until 2020, the volume of the digital data is expected to approximately double every two years, and 90% of the data available to us today were generated in just the last two years. Specifically in biological and biomedical research fields, we have witnessed the rapid advances in biotechnologies, especially the next-generation sequencing and single cell technologies, enabling the investigators to create massive amounts of data for genomics and translational research. While analysis of the data from single domain like mutations or gene expression is often the first choice in a project, integrative genomic approaches have more advantages such as robustness in detecting the biological signals or biomarkers and low false discovery rates due to the evidence from multiple domains. Therefore, we have steadily seen more integrative genomic studies during the past several years. Although these approaches are promising and powerful, there are many challenges that are required to be addressed by bioinformaticians, computational biologists, and other scientists. These challenges include, but are not limited to, data quality and process from different technology platforms, sample size and consistency, data missingness, incomplete and inaccurate knowledgebase (e.g., reference networks and pathways), false discovery, lack of novel algorithms for data integration, computational efficiency, data interpretation, and visualization.
Source: BioMed Research International
DOI: 10.1155/2015/725491
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $2,680
OpenAlex ID: W1820005161