Yu Kun-Hsing, Hart Steven N, Goldfeder Rachel, Zhang Qiangfeng Cliff, Parker Stephen C J, Snyder Michael
Biomedical Informatics Training Program, Stanford University 3165 Porter Dr., Room 2270, Palo Alto, CA 94304, USA,
Pac Symp Biocomput. 2017;22:635-639. doi: 10.1142/9789813207813_0058.
Precision medicine is a health management approach that accounts for individual differences in genetic backgrounds and environmental exposures. With the recent advancements in high-throughput omics profiling technologies, collections of large study cohorts, and the developments of data mining algorithms, big data in biomedicine is expected to provide novel insights into health and disease states, which can be translated into personalized disease prevention and treatment plans. However, petabytes of biomedical data generated by multiple measurement modalities poses a significant challenge for data analysis, integration, storage, and result interpretation. In addition, patient privacy preservation, coordination between participating medical centers and data analysis working groups, as well as discrepancies in data sharing policies remain important topics of discussion. In this workshop, we invite experts in omics integration, biobank research, and data management to share their perspectives on leveraging big data to enable precision medicine.Workshop website: http://tinyurl.com/PSB17BigData; HashTag: #PSB17BigData.
精准医学是一种健康管理方法,它考虑了个体在基因背景和环境暴露方面的差异。随着高通量组学分析技术的最新进展、大型研究队列的收集以及数据挖掘算法的发展,生物医学大数据有望为健康和疾病状态提供新的见解,这些见解可转化为个性化的疾病预防和治疗方案。然而,由多种测量方式生成的数PB生物医学数据给数据分析、整合、存储和结果解释带来了重大挑战。此外,患者隐私保护、参与医疗中心与数据分析工作组之间的协调以及数据共享政策的差异仍然是重要的讨论话题。在本次研讨会上,我们邀请了组学整合、生物样本库研究和数据管理方面的专家,分享他们对利用大数据实现精准医学的看法。研讨会网站:http://tinyurl.com/PSB17BigData;主题标签:#PSB17BigData