2016
DOI: 10.1038/nrd.2016.74
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Key factors for successful data integration in biomarker research

Abstract: Integrating a wide range of biomedical data such as that rapidly emerging from the use of next-generation sequencing is expected to have a key role in identifying and qualifying new biomarkers to support precision medicine. Here, we highlight some of the challenges for biomedical data integration and approaches to address them.

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Cited by 11 publications
(10 citation statements)
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“…Thus, any spurious findings (like data inconsistencies, outliers, etc.) would also be detected earlier, [29] which has an impact on the quality of the results.…”
Section: From Manual Extraction and Curation Of Literature Data To (Smentioning
confidence: 63%
See 1 more Smart Citation
“…Thus, any spurious findings (like data inconsistencies, outliers, etc.) would also be detected earlier, [29] which has an impact on the quality of the results.…”
Section: From Manual Extraction and Curation Of Literature Data To (Smentioning
confidence: 63%
“…Being embedded within an academic research environment, we have to deal with the fact that pre-clinical research data coming from the open domain is generally less well structured and curated than clinical data [29]. Thus, for the purpose of generating predictive models, it will in many cases appear inevitable to manually extract the pharmacological and other biomedical data directly from its primary source, i.e.…”
Section: From Manual Extraction and Curation Of Literature Data To (Smentioning
confidence: 99%
“…The integration of data, ranging from patient records to sequencing and multi-omics technologies, as well as databases from external data sources such as drug targets, molecular pathways or clinical trials ( Figure 1 ), is a challenge that requires coordinated action at different levels. 15 , 16 , 17 The IT infrastructure to manage such data (storage, provenance, security, sharing, user interfaces and process integration) is an important element to connect research and health care provided to patients. The analysis and interpretation of data requires the development of ‘integrative workflows’, combining multiple statistical, computational and mathematical techniques in a rational and reproducible process that can be implemented in software tools.…”
Section: Integrated Systems Medicine Workflowsmentioning
confidence: 99%
“…Perhaps the result of being an emerging field many of the categories of biological data captured do not have a conventional set of standards and as such many study reports and even repositories provide data with disparate format and labelling schemes [111]. Although the problem is computationally tractable, a lack of coherent standards can result in some difficulties when attempting to integrate different data types.…”
Section: Data Standardsmentioning
confidence: 99%