3.8 Article

Towards standardization guidelines for in silico approaches in personalized medicine

期刊

JOURNAL OF INTEGRATIVE BIOINFORMATICS
卷 17, 期 2-3, 页码 -

出版社

WALTER DE GRUYTER GMBH
DOI: 10.1515/jib-2020-0006

关键词

data integration; in silico modelling; personalized medicine; reproducibility; standards

资金

  1. European Union Horizon2020 framework programme of the European Commission [825843]
  2. H2020 Societal Challenges Programme [825843] Funding Source: H2020 Societal Challenges Programme

向作者/读者索取更多资源

Despite the ever-progressing technological advances in producing data in health and clinical research, the generation of new knowledge for medical benefits through advanced analytics still lags behind its full potential. Reasons for this obstacle are the inherent heterogeneity of data sources and the lack of broadly accepted standards. Further hurdles are associated with legal and ethical issues surrounding the use of personal/patient data across disciplines and borders. Consequently, there is a need for broadly applicable standards compliant with legal and ethical regulations that allow interpretation of heterogeneous health data through in silico methodologies to advance personalized medicine. To tackle these standardization challenges, the Horizon2020 Coordinating and Support Action EU-STANDS4PM initiated an EU-wide mapping process to evaluate strategies for data integration and data-driven in silico modelling approaches to develop standards, recommendations and guidelines for personalized medicine. A first step towards this goal is a broad stakeholder consultation process initiated by an EU- STANDS4PM workshop at the annual COMBINE meeting (COMBINE 2019 workshop report in same issue). This forum analysed the status quo of data and model standards and reflected on possibilities as well as challenges for cross-domain data integration to facilitate in silico modelling approaches for personalized medicine.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

3.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据