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Substantial data analysis improves gene-environmental correlation identification to help develop new treatment for multiple sclerosis
技术
- 分析与建模 - 大数据分析
- 分析与建模 - 数据挖掘
适用行业
- 教育
- 医疗保健和医院
适用功能
- 产品研发
用例
- 预测性维护
- 质量预测分析
服务
- 数据科学服务
挑战
作为世界领先的多发性硬化症 (MS) 研究中心之一,布法罗大学的研究人员希望识别和了解可能导致 MS 的环境因素。然而,基因环境研究为研究人员提供了大量数据,他们需要高性能处理能力和速度才能做出有意义的、可发表的发现。基因相互作用研究的一个主要挑战是以有助于拯救生命的速度分析大量数据集的爆炸式增长。
关于客户
纽约州立大学布法罗分校成立于 1846 年,是纽约州立大学系统中的旗舰机构。它是纽约州立大学系统中 64 个校区中规模最大、综合性最强的校区,也是世界领先的多发性硬化症 (MS) 研究中心之一的所在地。布法罗分校提供 100 多个本科学位、205 个硕士学位、84 个博士学位和 10 个专业学位。
解决方案
布法罗大学的研究人员使用一种强大的解决方案,该解决方案结合了数据仓库和分析功能,用于处理多发性硬化症 (MS) 疾病发现中基因相互作用的突破性数据挖掘方法所涉及的遗传变异的指数增长率。该解决方案有助于研究人员使用新算法并分析可能以千万亿为单位的海量数据,这在以前是不可能的,使他们能够检查可能导致多发性硬化症发展和进展的 2,000 多种遗传和环境因素。例如,研究人员可以分析维生素 D 代谢物与多发性硬化症中的残疾和脑萎缩的保护性关联,以及它与多发性硬化症在北纬地区更常见而在赤道地区不太常见的可能关联。反过来,新的见解可以帮助制定治疗和管理多发性硬化症的治疗和预防策略。
运营影响
数量效益
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