Workshop on Addressing Complexity in Multiscale Modeling and Analysis of Complex Data

解决复杂数据多尺度建模和分析中的复杂性研讨会

• 批准号: 1127047
• 负责人: Bulent Yener
• 金额: $ 1.5万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1127047&HistoricalAwards=false
• 关键词: Workshop; Addressing; Complexity; Multiscale; Modeling

All branches of modern science demand data, and modern data acquisition technologies in biomedical research have adapted to increase the volume of data by at least three orders of magnitude in the past three decades. Much of this data is rather complex. Complex data has three properties: high volume (e.g., genome data, gigabyte per person), multimodal (e.g., Time x Genes x Control), heterogeneous (e.g., different time scales, data feeds).How these data are analyzed now presents a major challenge in scientific discovery. Although a series of powerful algorithms for statistical learning can yield accurate predictions for pattern recognition problems, the increased power of these learning algorithms has come at the expense of a considerable increase in complexity for adjusting a large number of parameters in a high dimensional space. Yet, such mathematically sound models that are computationally stable and statistically meaningful still demand more data than most biology laboratories and clinics can provide. This workshop focus fits into the initiative between NSF and NIH to investigate developing science and technology for merging computational, physics based models with biology and medicine. This two day workshop at RPI fits into the initiative between NSF and NIH to investigate developing science and technology for merging computational, physics based models with biology and medicine. There will be a panel at the end of each day on a particular topic, e.g., how to integrate scales, how to deal with uncertainty and missing data, how to use data semantics in multi-scale modeling. The panel discussions will be organized into a report and will be made publicly available. The seminars and panel discussions will be recorded and made it available to public.

现代科学的所有分支都需要数据，而生物医学研究中的现代数据采集技术在过去三十年中已经适应了数据量至少三个数量级的增长。这些数据中有许多相当复杂。复杂数据具有三个属性：大容量（例如，基因组数据，每人千兆字节），多模式（例如，时间×基因×对照）、异质（例如，不同的时间尺度，数据馈送）。如何分析这些数据现在提出了科学发现的主要挑战。虽然一系列强大的统计学习算法可以为模式识别问题提供准确的预测，但这些学习算法的能力的增加是以在高维空间中调整大量参数的复杂性的显著增加为代价的。然而，这种数学上合理、计算稳定且具有统计意义的模型仍然需要比大多数生物实验室和诊所能够提供的更多数据。 这个研讨会的重点符合NSF和NIH之间的倡议，以研究发展科学和技术，将基于计算的物理模型与生物学和医学相结合。 这个为期两天的研讨会适合NSF和NIH之间的倡议，以调查发展科学和技术，将基于计算的物理模型与生物学和医学相结合。 每天结束时将有一个关于特定主题的小组讨论，例如，如何整合尺度，如何处理不确定性和缺失数据，如何在多尺度建模中使用数据语义。小组讨论将编入一份报告，并将向公众提供。研讨会和小组讨论会将被记录下来并向公众提供。