Literature DB >> 27807750

Biobanks and Their Clinical Application and Informatics Challenges.

Lan Yang1, Yalan Chen1,2, Chunjiang Yu3, Bairong Shen4.   

Abstract

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.

Entities:  

Keywords:  Biomedical; Harmonization; Personalized medicine; Standardization

Mesh:

Year:  2016        PMID: 27807750     DOI: 10.1007/978-981-10-1503-8_10

Source DB:  PubMed          Journal:  Adv Exp Med Biol        ISSN: 0065-2598            Impact factor:   2.622


  3 in total

1.  Metabolomics technology and bioinformatics for precision medicine.

Authors:  Rajeev K Azad; Vladimir Shulaev
Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

2.  Quality Assurance After a Natural Disaster: Lessons from Hurricane Sandy.

Authors:  Collin Dickerson; Yanshen Hsu; Sandra Mendoza; Iman Osman; Jennifer Ogilvie; Kepal Patel; Andre L Moreira
Journal:  Biopreserv Biobank       Date:  2018-01-03       Impact factor: 2.300

3.  Identifying Datasets for Cross-Study Analysis in dbGaP using PhenX.

Authors:  Huaqin Pan; Vesselina Bakalov; Lisa Cox; Michelle L Engle; Stephen W Erickson; Michael Feolo; Yuelong Guo; Wayne Huggins; Stephen Hwang; Masato Kimura; Michelle Krzyzanowski; Josh Levy; Michael Phillips; Ying Qin; David Williams; Erin M Ramos; Carol M Hamilton
Journal:  Sci Data       Date:  2022-09-01       Impact factor: 8.501

  3 in total

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