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

现代科学的所有分支都需要数据，生物医学研究中的现代数据采集技术已经适应了在过去三十年中至少增加三个数量级的数据量。其中很多数据相当复杂。复杂数据有三个特性：高容量（例如，基因组数据，每人千兆字节），多模式（例如，时间x基因x控制），异构（例如，不同的时间尺度，数据馈馈线）。如何分析这些数据现在是科学发现的一个重大挑战。尽管一系列用于统计学习的强大算法可以对模式识别问题产生准确的预测，但这些学习算法的增强功能是以在高维空间中调整大量参数的复杂性大大增加为代价的。然而，这种数学上合理、计算上稳定、统计上有意义的模型仍然需要比大多数生物学实验室和诊所所能提供的更多的数据。本次研讨会的重点与美国国家科学基金会和美国国立卫生研究院之间的倡议相吻合，该倡议旨在研究将基于计算、物理的模型与生物学和医学相结合的科学技术。这个为期两天的RPI研讨会符合美国国家科学基金会和美国国立卫生研究院之间的倡议，旨在研究将基于计算的物理模型与生物学和医学相结合的科学技术。每天结束时都会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。小组讨论将组织成一份报告，并将向公众提供。讨论会和小组讨论将被记录下来，并提供给公众。

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.