Key factors for successful data integration in biomarker research

Elefsinioti, Antigoni; Bellaire, Tanja; Wang, Albert; Quast, Karsten; Seidel, Henrik; Braxenthaler, Michael; Goeller, Gernot; Christianson, Anastasia; Henderson, David; Reischl, Joachim

doi:10.1038/nrd.2016.74

Cited by 11 publications

(10 citation statements)

References 1 publication

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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%

“…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%

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Empowering pharmacoinformatics by linked life science data

Goldmann

Zdrazil

Digles

et al. 2016

J Comput Aided Mol Des

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With the public availability of large data sources such as ChEMBLdb and the Open PHACTS Discovery Platform, retrieval of data sets for certain protein targets of interest with consistent assay conditions is no longer a time consuming process. Especially the use of workflow engines such as KNIME or Pipeline Pilot allows complex queries and enables to simultaneously search for several targets. Data can then directly be used as input to various ligand- and structure-based studies. In this contribution, using in-house projects on P-gp inhibition, transporter selectivity, and TRPV1 modulation we outline how the incorporation of linked life science data in the daily execution of projects allowed to expand our approaches from conventional Hansch analysis to complex, integrated multilayer models.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-016-9990-4) contains supplementary material, which is available to authorized users.

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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%

Section: From Manual Extraction and Curation Of Literature Data To (Smentioning

confidence: 99%

Empowering pharmacoinformatics by linked life science data

Goldmann

Zdrazil

Digles

et al. 2016

J Comput Aided Mol Des

View full text Add to dashboard Cite

show abstract

“…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%

Whither systems medicine?

et al. 2018

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New technologies to generate, store and retrieve medical and research data are inducing a rapid change in clinical and translational research and health care. Systems medicine is the interdisciplinary approach wherein physicians and clinical investigators team up with experts from biology, biostatistics, informatics, mathematics and computational modeling to develop methods to use new and stored data to the benefit of the patient. We here provide a critical assessment of the opportunities and challenges arising out of systems approaches in medicine and from this provide a definition of what systems medicine entails. Based on our analysis of current developments in medicine and healthcare and associated research needs, we emphasize the role of systems medicine as a multilevel and multidisciplinary methodological framework for informed data acquisition and interdisciplinary data analysis to extract previously inaccessible knowledge for the benefit of patients.

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“…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%

‘Big data’ approaches for novel anti-cancer drug discovery

Benstead-Hume

Wooller

Pearl

2017

Expert Opinion on Drug Discovery

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The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Recent advances in platform technologies and the increasing availability of biological 'big data' are providing an unparalleled opportunity to systematically identify the key genes and pathways involved in tumorigenesis. The discoveries made using these new technologies may lead to novel therapeutic interventions. Areas covered: The authors discuss the current approaches that use 'big data' to identify cancer drivers. These approaches include the analysis of genomic sequencing data, pathway data, multi-platform data, identifying genetic interactions such as synthetic lethality and using cell line data. They review how big data is being used to identify novel drug targets. The authors then provide an overview of the available data repositories and tools being used at the forefront of cancer drug discovery. Expert opinion: Targeted therapies based on the genomic events driving the tumour will eventually inform treatment protocols. However, using a tailored approach to treat all tumour patients may require developing a large repertoire of targeted drugs.

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Key factors for successful data integration in biomarker research

Cited by 11 publications

References 1 publication

Empowering pharmacoinformatics by linked life science data

Empowering pharmacoinformatics by linked life science data

Whither systems medicine?

‘Big data’ approaches for novel anti-cancer drug discovery

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