BioBankWarden: A web-based system to support translational cancer research by managing clinical and biomaterial data

Ferretti, Yuri; Miyoshi, Newton Shydeo Brandão; Silva, Wilson A.; Felipe, Joaquim Cezar

doi:10.1016/j.compbiomed.2015.04.008

Cited by 7 publications

(1 citation statement)

References 12 publications

(12 reference statements)

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“…A total of 241 articles were identified in the literature search [ 11 , 13 - 19 , 21 - 29 , 31 , 33 - 35 , 37 , 43 , 44 , 47 - 49 , 52 - 264 ]; the largest number of articles were identified in IEEE Xplore (n=112), followed by MEDLINE (n=95), and Google Scholar (n=71). After removing duplicates (n=24), we added 3 articles that were frequently cited in the selected articles but were missing from our search results [ 30 , 36 , 42 ]. Three articles [ 38 , 40 , 45 ] were further added that provided additional details relevant to the review topic.…”

Section: Resultsmentioning

confidence: 99%

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Gagalova¹,

Elizalde²,

Portales-Casamar³

et al. 2020

JMIR Form Res

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Background Integrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade. Objective The architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation. Methods We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices. Results Despite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making). Conclusions IDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.

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Section: Resultsmentioning

confidence: 99%

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Gagalova¹,

Elizalde²,

Portales-Casamar³

et al. 2020

JMIR Form Res

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Metabolomics: From Scientific Research to the Clinical Diagnosis

Estrella-Parra

Espinosa-González

García-Bores

et al. 2022

Principles of Genetics and Molecular Epidemiology

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Biobanks and Their Clinical Application and Informatics Challenges

Yang

Chen

et al. 2016

Advances in Experimental Medicine and Biology

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Biobanks are one of the most important biomedical research resources and contribute to the development of biomarker detection, molecular diagnosis, translational medicine, and multidisciplinary disease research, as well as studies of interactions between genetic and environmental or lifestyle factors. Aiming for the wide clinical application of biobanks, biobanking efforts have recently switched from a focus on accumulating samples to both formalizing and sustaining collections in light of the rapid progress in the fields of personalized medicine and bioinformatics analysis. With the emergence of novel molecular diagnostic technologies, although the bioinformatics platform of biobanks ensures reliable bioinformatics analysis of patient samples, there are a series of challenges facing biobanks in terms of the overall harmonization of policies, integrated processes, and local informatics solutions across the network. Further, there is a controversy regarding the increased role of ethical boards, governance, and accreditation bodies in ensuring that collected samples have sufficient informatics capabilities to be used in biobanks. In this volume, we present a selection of current issues on the inevitable challenges of the clinical application of biobanks in informatics.

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BioBankWarden: A web-based system to support translational cancer research by managing clinical and biomaterial data

Abstract: BioBankWarden can be used to store and retrieve specific information from different clinical fields linked to biomaterials collected from patients, providing the functionalities required to support translational research in the field of cancer.

Cited by 7 publications

References 12 publications

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Metabolomics: From Scientific Research to the Clinical Diagnosis

Biobanks and Their Clinical Application and Informatics Challenges

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