每个人都在谈论科学和分析数据。
这是多么AbbVie方法。
每个人都在谈论科学和分析数据。这是多么AbbVie方法。
What do a genetics pioneer, medicinal chemist and IT manager have in common? Find out how they’re teaming up to lead one of the largest data initiatives in the industry.\r\n"}}" id="text-6854280ab5" class="cmp-text">
遗传学的先驱、药用化学家和IT经理有何共同之处?找出他们合作领导最大的数据计划。
The average person generates 1.7 megabytes of data every second. That’s enough to fill a laptop hard drive every week. In science and medicine, the amount of data we have access to is also growing — in fact, it’s doubling every 18 months. But with so much data being generated, how can scientists best collect, analyze, and apply it to help improve patients’ lives and revolutionize the healthcare industry?\r\n
At AbbVie, diverse teams of life scientists, data scientists and engineers are creating new ways of doing just that. We call this field data convergence, and it’s evolving as quickly as we generate data.
\r\nThree leaders share their insights on how to harness data and what they’ve learned building a team of experts with unique skillsets.
\r\n"}}" id="text-0ed1e1496e" class="cmp-text">一般人每秒钟产生1.7 mb的数据。足够填充每周一个笔记本硬盘。在科学和医学,我们获得的数据量也越来越多——事实上,每18个月翻一番。但产生如此多的数据,科学家们如何最好的收集、分析和应用,以帮助改善病人的生活和改变医疗行业吗?
在不同团队的生命科学家AbbVie,数据科学家和工程师创建的新方法。我们称之为字段数据融合,尽快发展我们生成数据。
三位领导人分享他们的见解如何利用数据和他们所学到建立一个专家小组具有独特的一套技能。
问:什么是数据集成、数据融合,它叫做AbbVie ?
Howard Jacob, Ph.D., vice president of genomics research and head of data convergence: Convergence is in some sense exactly what it sounds like — it’s bringing together different types of data to create knowledge. We have access to so much data here at AbbVie: preclinical and clinical trial data, public databases, data from scientific publications, and a million genomes — that’s a lot of data!\r\n
So, data convergence was born out of the question “how do we take all of that information and put it together so that we can drive new knowledge much faster?
\r\n"}}" id="text-f928e028be" class="cmp-text">霍华德·雅各布博士,基因组学研究的副总裁和头部的数据融合:收敛在某种意义上正是这听起来像——这是结合不同类型的数据创建知识。这里有如此多的数据访问AbbVie:临床前和临床试验数据,公共数据库,科学出版物的数据,和一百万个基因组——这是很多数据!
因此,数据融合出生的问题“我们如何把所有的信息放在一起,这样我们可以更快地推动新知识?
问:什么是数据科学技能需要解锁新的知识吗?
Jennifer Van Camp, Ph.D., senior director and research fellow, R&D, information research: There are a lot of disciplines that are represented on data science teams at AbbVie that one doesn't necessarily think of as typical for the pharmaceutical industry. We have electrical engineers with experience in signal and image processing, computational linguists, data engineers, and life scientists like me on our data science teams. It’s the mix of different core strengths and experiences that make progress possible.\r\n"}}" id="text-2d62f9d7fe" class="cmp-text">
詹妮弗·范营地,博士,研究员,高级主管研发、信息研究:有很多学科在AbbVie数据科学团队的代表,一个并不一定认为是典型的制药工业。我们有有经验的电气工程师在信号和图像处理,计算语言学家、工程师、数据和生命科学家像我一样对我们的数据科学团队。这是混合不同的核心优势和经验可能取得进展。
问:什么是一些数据科学工具AbbVie正在建设的桥梁从数据到知识和见解?
Jacob: We’ve built a central knowledge platform called the ARCH, which stands for AbbVie R&D Convergence Hub. It pulls together all these different data sets into one place. It incorporates different types of information tools that allow us to ask new questions and gain additional insights. With ARCH, we’re able to learn new things about disease definitions, discover new indications, and take a deeper look into understanding the molecular underprint of a disease. In a sense, it democratizes the data.\r\n
Brian Martin, head of artificial intelligence & search fellow, R&D, information research: The powerful thing about the ARCH is that it’s a knowledge platform versus a data platform. What makes it that way is our scientists and having them explain what makes a specific piece of data information meaningful.
\r\n"}}" id="text-3ed941c502" class="cmp-text">雅各:我们已经建立了一个中央知识平台称为拱,即收敛AbbVie研发中心。它将所有这些不同的数据集聚拢到一个地方。它包含不同类型的信息工具,允许我们问新的问题和获得更多的见解。弓,我们能够学习新事物关于疾病的定义,发现新的适应症,深入了解一下理解疾病的分子underprint。从某种意义上讲,它把数据。
布莱恩·马丁的人工智能和搜索的研发、信息研究 拱 :强大的事情是,这是一个知识的平台和一个数据平台。这样使我们的科学家,让他们解释是什么让一个特定的数据信息有意义。
问:数据融合可以帮助我们识别潜在的新疗法对未满足病人的需要吗?
Martin: The real-world data we have access to provides us with the opportunity to ask ourselves questions regarding patients’ experiences to quantify their unmet needs from various perspectives. For example, we can now look at preclinical data alongside clinical data and data from insurance claims. This can help us connect the various impacts that a certain compound might have on patients. That ability to bridge the gap from the real-world data back to preclinical data in the lab, that's the power of integration to affect the way we bring assets into our pipeline.\r\n"}}" id="text-a9618ea9c7" class="cmp-text">
马丁:我们获得的真实数据为我们提供了机会问自己问题病人的经验从各种角度量化他们的未满足的需求。例如,我们可以现在看看临床前和临床数据和数据从保险索赔。这可以帮助我们连接的各种影响某一化合物可能对病人。从真实数据能力的桥梁回到实验室,临床前数据集成的力量的影响我们资产进入管道。
问:什么建议的人从事数据收敛感兴趣?
Van Camp: I don’t think there is a single answer – there’s not one class you should take or a degree you need. But I always believe in being relentlessly curious. As an organization, industry, and a society, we have to keep asking those hard questions that we can't answer.\r\n
Watch AbbVie’s LinkedIn Live event: “What’s the Formula? Tackling the digital health revolution” for more from AbbVie’s data convergence leaders.
\r\n"}}" id="text-02a1e383af" class="cmp-text">车营:我不认为有一个答案——不是你应该采取一个类或一个你需要的程度。但是我总是相信被无情地好奇。作为一个组织,行业,一个社会,我们必须不断地问那些我们无法回答的难题。
看AbbVie LinkedIn生活事件:“公式是什么?处理健康问题的数字革命从AbbVie”更多的数据融合的领导人。
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