{"title":"A big data platform to enable integration of high quality clinical data and next generation sequencing data","authors":"Joel Haspel","doi":"10.1016/j.nhtm.2014.11.011","DOIUrl":null,"url":null,"abstract":"<div><p><span><span><span>Today, personalized medicine is closer to reality than ever before through targeted treatment, however, the substantial increase in data correspondingly requires scalable systems to continue to effectively manage the data and to remain current with advancing technology. As organizations move to advance </span>translational research to achieve personalized medicine, researchers and clinicians must manage informatics, however, there is a shortage of fully integrated informatics solutions that integrate, store, and analyze clinical and </span>omics data from diverse sources – generated in-house as well as public consortiums. Many researchers and clinicians must rely on </span>bioinformaticians<span> to perform mundane data management tasks in order to validate a simple hypothesis. Oracle Health Sciences Translational Research Center provides a complete and scalable informatics solution, with centralized data storage and analysis across genetic information areas (genomics, transcriptomics, and proteomics), vendor platforms, biological data types, and clinical data sources. Organizations such as Cancer Research UK, Erasmus MC, MD Anderson Cancer Center and UPMC have adopted this solution and are evaluating treatment responses for similar patients in a self-sufficient manner, ultimately shortening the biomarker development cycle and accelerating the adoption of personalized medicine.</span></p></div>","PeriodicalId":90660,"journal":{"name":"New horizons in translational medicine","volume":"2 2","pages":"Pages 57-58"},"PeriodicalIF":0.0000,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.nhtm.2014.11.011","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"New horizons in translational medicine","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2307502314000289","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Today, personalized medicine is closer to reality than ever before through targeted treatment, however, the substantial increase in data correspondingly requires scalable systems to continue to effectively manage the data and to remain current with advancing technology. As organizations move to advance translational research to achieve personalized medicine, researchers and clinicians must manage informatics, however, there is a shortage of fully integrated informatics solutions that integrate, store, and analyze clinical and omics data from diverse sources – generated in-house as well as public consortiums. Many researchers and clinicians must rely on bioinformaticians to perform mundane data management tasks in order to validate a simple hypothesis. Oracle Health Sciences Translational Research Center provides a complete and scalable informatics solution, with centralized data storage and analysis across genetic information areas (genomics, transcriptomics, and proteomics), vendor platforms, biological data types, and clinical data sources. Organizations such as Cancer Research UK, Erasmus MC, MD Anderson Cancer Center and UPMC have adopted this solution and are evaluating treatment responses for similar patients in a self-sufficient manner, ultimately shortening the biomarker development cycle and accelerating the adoption of personalized medicine.
如今,通过有针对性的治疗,个性化医疗比以往任何时候都更接近现实,然而,数据的大幅增加相应地需要可扩展的系统来继续有效地管理数据,并与先进的技术保持同步。随着组织推进转化研究以实现个性化医疗,研究人员和临床医生必须管理信息学,然而,缺乏完全集成的信息学解决方案来集成、存储和分析来自不同来源的临床和组学数据-内部生成以及公共联盟。为了验证一个简单的假设,许多研究人员和临床医生必须依靠生物信息学家来执行日常的数据管理任务。Oracle健康科学转化研究中心提供了一个完整的、可扩展的信息学解决方案,具有跨遗传信息领域(基因组学、转录组学和蛋白质组学)、供应商平台、生物数据类型和临床数据源的集中数据存储和分析。英国癌症研究中心(Cancer Research UK)、伊拉斯谟癌症中心(Erasmus MC)、MD Anderson癌症中心(MD Anderson Cancer Center)和UPMC等组织已经采用了这种解决方案,并以自给自足的方式评估类似患者的治疗反应,最终缩短了生物标志物的开发周期,加速了个性化医疗的采用。